Sports Picks For Sale - Hollywood Sports

Hot Streaks and Achievements

  • *60%* 104 of 172 All-Sports since 8/1 (9-2 since Monday!)
  • *63%* 56 of 89 NFL (8-2 run) and 31 of 46 (67%) NFL 25* runs through 10/22!
  • *67%* 48 of 72 MLB and 18-8-1 in this MLB postseason through 10/22!

Biography

Frank Sawyer’s Hollywood Sports offers unmatched handicapping analysis over a diverse array of sports for his A-List clients.

Active since: 1997

Location: Las Vegas, NV

Frank Sawyer founded Hollywood Sports in 1997 at the urging of his celebrity clients, who recognized (and were greatly benefiting from) Frank’s unique handicapping skill set.  Over the last 23 years, Frank’s had such spectacular success — with both his picks and his ever-expanding network of clients — that he relocated his business to Las Vegas, Nevada.  

Certainly, Frank has the deep knowledge to find the edge in the numbers, but perhaps his greatest edge results from his drive to outwork his competition.  And Frank’s hard work and research is instantly illustrated by the extensive reports that detail his decisions.  He does this for two reasons: (1) clients deserve to see the work that goes into a conclusion and (2) Frank’s attention to detail within these reports validates to his clients that he continues to work tirelessly to unearth winning angles.  

Also at the core of Frank’s success is a proven methodology.  His model starts by investing a significant amount of time analyzing each team in the sports he handicaps to serve as a foundation for that season.  Frank then combines his team assessments with empirical situational handicapping angles, along with the latest in cutting edge analytics, to identify value in the betting line.  

Frank believes long-term success not only involves a continuous deep-dive into sports that he covers, but also an expansion into new events that can provide additional tools to use in his handicapping toolbox — with the ultimate goal of rewarding his clients for their loyalty over the years.  With that ambition in mind, Frank has expanded his areas of interest and scrutiny to include the English Premier League, the PGA Tour, the WNBA, and UFC/Boxing in recent years, to go along with the major four sports of football, basketball, baseball, and hockey (along with the Canadian Football League, Horse Racing, and World Cup Soccer) that he has specialized in since he began Hollywood Sports in 1997. 

Frank uses a 10*, 20*, and 25* rating system that also serves a guide for money management: 10* plays warrant a standard bet; 20* plays should see a doubling of that standard bet; while 25* plays are Frank’s highest-rated selections with the recommendation to invest 2.5 times the standard bet.  Frank’s signature play is his “A-List” release, which is reserved for rare and elite betting opportunities.  For Frank’s futures reports, or for events with odds such as PGA events or horse races, Frank usually offers three recommendations in his betting report: his Best Bet for the event; his Top Overlay Bet which represents his best value play relative to the odds for the event; and his Long Shot Bet for a big underdog which offers value with its potential big payout.

The Cincinnati Bengals Laying More than a Touchdown? A Brave New World!

Friday, Oct 01, 2021

Successful long-term gambling in football requires the willingness to back bad teams — the ones that the betting public does not want to touch. That is what I told clients on the last day in September with the Thursday Night game to begin Week Four in our play on the Jacksonville. Admittedly, anyone betting this game was forced to invest in a bad team since the Jaguars were playing the Cincinnati Bengals. The Bengals’ faithful saw their team in a situation that they have not enjoyed in many years. With the Bengals laying 7.5-points in most spots, it was just the fourth time in head coach Zac Taylor’s three-year year tenure as the head coach of the Bengals that his team was the point spread favorite — and it was the first time that Cincinnati was every laying more than a field goal in Taylor’s tenure. The Bengals lost the two games they were favored in Taylor’s rookie season. Their lone win and point spread cover as a favorite under Taylor was their 33-25 win against the Jaguars last October as a 1-point favorite. With Urban Meyer now the head coach and Trevor Lawrence under center, this was a different Jacksonville team who went into halftime with a 14-0 lead before losing by a 24-21 score. Meyer’s decision to forego kicking a field goal at the end of the first half may have won him fans from the football analytics community live-tweeting during the game, but Jacksonville could have sure used three more points at the end of that game. Frankly, the Bengals were primed for a letdown after their triumph against their heated AFC North rival in the Steelers. Did Cincinnati win that game — or did Pittsburgh lose it? The Bengals gained only 268 yards. They ran only 42 plays while the Steelers took 77 snaps under center. Cincy converted only three of nine of their third-down opportunities — a disturbing continuation from last season when they were 30th in the NFL by converting 36.2% of their third-down opportunities. But the Bengals were able to pull off their second upset victory of the season after beating Minnesota in Week One as an underdog. I like quarterback Joe Burrow — and would like him even more if played behind an even average offensive line. The Bengals allowed 48 sacks last season — and they have surrendered 10 sacks so far this season despite the Steelers failing to sack Burrow even once. Cincinnati got the memo to run the ball more after Burrow’s season-ending injury last year just to keep Burrow out of harm’s way. The Bengals are running the ball 52% of the time. But that also means that they are scoring only 22.7 Points-Per-Game while averaging just 291.0 total YPG. I am not sold on Taylor as a head coach, yet another beneficiary for once being in the same room as Sean McVay. With offensive coordinator Brian Callahan and defensive coordinator Lou Anarumo, he has assembled a support staff that will not threaten his authority. Who knows what the future will hold this season regarding how I feel about this Bengals team. Bettors and handicappers make a mistake to get stuck in preconceived notions. Some teams improve, while other teams falter. However, I do not see myself backing Zac Taylor’s team too often if they find themselves laying more than a touchdown again this season. I don’t think I have the courage quite yet for that Brave New World.Best of luck — Frank.

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Joe Judge on Analytics (and the Self-Own of his Critics)

Thursday, Sep 30, 2021

New York Giants head coach Joe Judge got in trouble with the self-professed smartest guys online when he failed to demonstrate proper deference to the Analytics God. Here were Judge’s comments in a press conference earlier this week. "Analytics is just a tool. It’s nice to look at the numbers and how they go through the flow of the game, but the analytics change based on the opponent, based on who you have available for the game, and how the flow of the game is going, too. You can look at a stat sheet all ya want. I promise ya if Excel was gonna win football games, Bill Gates would be killing it right now. But you've got to take those numbers as a tool and go ahead and factor in how your team's playing at the time and how the opponent is as well. You’ve got to measure your strengths and weaknesses against the opponent every time and then also in terms of the flow of the game.”Judge’s tone and suggestion that analytics can be expressed by a simple Excel one-sheet have been ridiculed by many who profess to be devotees to football analytics, whatever that means (more on that below). Football statistics and metrics are attractive for a variety of reasons. One of the appeals for those who like to invoke “The Numbers” is that it offers the allure of a superior intelligence without having to do the work of province a warrant to defend the claim. And when capital-A “Analytics” is used as the cover, then the individual gets hide find the protection of the mob. “Joe Judge doesn’t believe in analytics, what an idiot” is the perfect sentiment for those whose deepest thoughts come in 280 characters or less. As an aside, those familiar with my work know that I value analytics. It was the addition of basketball analytics into my handicapping toolbox that helped conclude the 2010 NCAA Tournament on a 20-4 ATS run after the first day of the Big Dance. Sabermetrics has been a foundational aspect of my handicapping of MLB regarding the respective starting pitchers since Day One in the field. Expected goals (xG) play a critical role in my handicapping of soccer and the NHL. Analytics plays a role in my football handicapping, but I consider much of that work so fundamentally flawed that I take many of their predictive numbers with a large grain of salt. I am not anti-analytics. I am anti-lazy thinking (and making claims without warrants). Interestingly, the criticism of Judge’s comments exposes some assumptions many (not all) in the football analytics community make that would probably not withstand scrutiny if put under a microscope. Many football analytics adherents seem to advocate the there is a One Truth exposed by analytics as The Way. “The numbers say go for it on fourth-and-one.” Well, where did those numbers come from? League-wide data? What years? Does it include the pandemic season without fans in the stands? How big is the sample size? How big should the sample size be? Does that league-wide data treat Derrick Henry’s fourth-and-one numbers as equally relevant to those numbers for, say, Theo Riddick? Is Judge wrong when he argues that “the analytics change based on the opponent, based on who you have available for the game and how the flow of the game is going, too”? Do the football analytics folks really want to suggest that there is no statistical difference between Saquon Barkley rushing the football on 4th-and-one versus Gary Brightwell, their sixth-round pick from Arizona? Is there no statistical difference between attempting a fourth-and-down rushing play against the Tampa Bay run defense as opposed to the Detroit run defense? Another assumption many in the analytics community makes is that every statistical moment is the same. Many in the analytics basketball community presume this when defending the use of shooting tons of 3-pointers. They are later surprised when the Houston Rockets missed 27 straight 3-pointers in Game Seven of the 2018 NBA Western Conference Finals. Too many in the quantitative field rigidly support the belief that those 27 misses could have just as easily happened on a Tuesday night in February of the regular season. To suggest otherwise is to expose a fundamental problem with their project: that the numbers may not apply as nicely to the situation at hand. For those interested in nuance and perspective, this revelation is not threatening. For those who have a vested interest — financial or otherwise — this proposition is an existential threat. It is not a radical idea to offer that basketball teams not good at shooting 3-pointers should shoot less of them (in place of higher percentage shots for their team’s skillsets). This is probably true even after confronting the fact that 3-point baskets offer 50% more value than 2-point baskets. Is it a radical idea by Judge that perhaps the percentages for his team on 4th-and-one may not be as prolific as that of the Kansas City Chiefs?And is it a radical idea that going for it on fourth-and-one (to continue using this one example since it most often gets deployed by the football analytics folks as if there is capital-t Truth answer to this question that can fit on an Excel sheet) that the answer may change based on field position, game score, and how much time is left in the game?The other major sports are getting better at appreciating that rather than establishing an Analytics Department to expose the Truth, instead the use of numbers and statistics is fluid that can be exploited for a strategic advantage. All numbers are not created equal because all formulas are not created equal. Some analytics are simply more illuminating. While yards per game offer some value, yards-per-play may offer a more insightful perspective. Just like the NFL has 32 unique scouting departments that make different evaluations, the league will eventually have 32 unique analytics departments that have differing views — and this is before head coaches then interpret that data based on his available personnel, the score of the game, and the moment in the game. Just using the analytics umbrella does not provide invincibility against potential critique. The audience is not privy to the NextGen formulas used to develop their stats ESPN hawks (in partnership) regarding what a coach should do in a certain situation. The broadcast is not a math class, but it is theater. It’s a smaller narrative within the bigger story. Many statistical models in football do not put any value on first downs and time of possession. Do they do this because they disagree with many football coaches who find both those aspects of the game critical? Or, do they do it because it is more convenient to ignore those facets of the game? Using Yards-Per-Play as the base unit of efficiency is easier than the messy work of determining how to value the reset of downs offered by generating 10 yards in four plays. There are many differences between Joe Judge and his critics, but one I would like to close with is this: it is only Judge that risks losing his job if gets a football decision wrong. His critics risk nothing. Many of his critics have a vested financial interest in presenting their criticism since that it is the foundation of their business model. When contemplating going for it on fourth-and-one (to torture this one example), I suspect there would be a quick about-face in opinion if the critic were to lose their job if they got the decision wrong. In fact, I suspect all it would take for many of his critics to demonstrate caution and nuance would be the mere threat of being blocked or unliked if their opinion from the cheap seats turned out to be correct. Best of luck — Frank.

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Handicapping the 2021 NFLx Preseason: Autopsy Report

Tuesday, Aug 31, 2021

We concluded a 7-4 mark in the NFLx preseason by winning out 25* NFLx Preseason Game of the Year on Sunday with the Cleveland Browns minus the points against Atlanta. NFL preseason football is beatable — but it requires using a different set of methods than handicapping the NFL in the regular season. We will begin the 2022 NFLx preseason on a 16 of 22 (73%) NFLx run and a longer-running 46 of 74 (62%) NFLx preseason mark. We won six of our seven highest-rated 25* NFLx preseason plays this year, furthering a 13 of 14 (93%) NFLx 25* preseason run going back to 2019 and a longer running 26 of 34 (76%) preseason mark with 25* plays. Being a little choosier while being patient has helped to make decisions as to when to invest in a preseason situation. Handicapping the preseason in the NFL (successfully) is not the same as it was five years ago. Back then, deciphering edges against the point spread mostly involved the qualitative assessment and comparison of both team’s rosters — especially at quarterback. Getting a book on the philosophy each head coach had regarding how they used preseason games has always been important as well — but Sean McVay’s commitment to not play any starters in the preseason established a school of thought that many of his peers have adopted. After no preseason last year because of the pandemic and the league’s shift to just three preseason games, a new dynamic has taken hold this year. It had been conventional wisdom for most head coaches to use the third preseason game as the dress rehearsal game where he has his starters get in their most minutes — and then the last preseason game is used exclusively to make the final roster decisions. Not playing starters in the final preseason game also gave them a week of rest before the regular season while protecting them from short-term injuries that could threaten their status for the start of the season. But with the NFL having next week off before the start of the regular season, head coaches face a dilemma: not playing their starters in this third preseason game could risk them being rusty. First and foremost, handicapping the preseason requires understanding what philosophical approach the respective head coaches are using for the preseason game. Head coaching team trends that are specific to the preseason can help, but the loss of the fourth preseason game makes the sample size thinner even for veteran NFL head coaches. Following beat reporters who can provide insight regarding how the head coach plans to use the game is helpful. Often this news does not break until the day of the game. Favorites were 23-22-2 ATS this preseason. Going back to include the preseason data from 2019, underdogs hold a narrow 54-47-5 ATS mark. The Under was 28-20 this preseason. After the Under was 35-27-1 in the 2019 preseason, the Under is now 63-47-1 in the last two preseasons. I started tracking ATS numbers for games that conclude a three-day joint practice session. Oftentimes, head coaches use the controlled scrimmage environment (where quarterbacks wear the red jersey that prohibits getting hit) to work on their more sophisticated plays and packages. Did that affect the results of the exhibition game? In 2019, dogs were 6-5 ATS in preseason games that concludes joint practices. The Under was 8-3 in those games. In 2021, dogs were 7-5 ATS, but the Over was 8-4. Overall, underdogs are 13-10 ATS in preseason games that conclude joint practices the last two seasons. The Under is 12-11 in those situations the last two seasons. While the sample size remains small, it appears there is no angle to be gleaned from those joint practice situations. Duly noted for 2022! Best of luck — Frank.

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The Lost and Last Days of Scott Frost at Nebraska

Tuesday, Aug 31, 2021

Nebraska is (most likely) going to have a new coach for their fabled college football team next season. The writing may have been written on the wall earlier this month when the head coach Scott Frost was placed under investigation for the improper use of consultants and analysts during games and practices. The NCAA investigation may extend to include possible off-campus workouts and practices that Frost organized despite guidelines that prohibited those activities during the early stages of the COVID pandemic. While Frost is a Nebraska alumnus, so too is athletic director Trev Alberts who was hired for the position after Frost’s tenure had started — so he is not an Alberts hire. But if Alberts still had a chance to salvage things this season, the Cornhuskers’ 30-22 upset loss at Illinois as a 6-point favorite likely sealed his fate. It is not so much the upset loss that was infuriating (especially for backers like me) as it was the continued mental mistakes that have become endemic for this program under Frost. Nebraska surrendered a safety that opened the scoring. They then went on to dominate the first quarter — and Adrian Martinez’s inability to complete a potential touchdown pass to a wide-open receiver to take a 14-2 lead led to the Huskers settling for a field goal … which they missed. The Nebraska defense remained dominant — but when an interception on the Illini’s side of the field was nullified by a roughing-the-passer penalty. The momentum shifted, Illinois scored a touchdown on that drive — and they recovered a fumble at the Cornhuskers’ 41-yard line which they returned for a touchdown to seize a 16-9 halftime lead. The Illini scored two more touchdowns in the third quarter to take a 30-9 lead before the Huskers scored two touchdowns to make the final score look respectable. Nebraska won the yardage battle by a 392 to 326 margin — yet Frost’s team was not competitive on the scoreboard. If that performance did not seal Frost’s fate in the eyes of Albert after the game, the coach’s comments after the game probably did. Uttered Frost about the play of his offense: “About half of our game plan was out the window when they lined up how they did.”To paraphrase the football wisdom of Bugs Bunny: “What a maroon!”As if accidentally conceding that one’s offensive acumen does not include the ability to make adjustments (after a month of practice), admitting to being be owned by Illinois coach Bret Bielema in his first game back in the Big Ten along with his defensive coordinator Ryan Walters who he poached from Missouri is not going to go over well with Albert who is one of the most fabled “blackshirts” in the history of the Nebraska program. Frost’s tenure with the Cornhuskers has been a disaster. On the field, the team is now 12-21 with seven upsets losses in his fourth season. They are 9-22 in Big Ten play. Supposedly an offensive guru, Nebraska scored only 23.1 PPG last season which was the lowest mark for the football team this century. The terrible play of the special teams has been the most consistent aspect of the program under Frost. Two of his best players last year, quarterback Luke McCaffrey and wide receiver Wan’Dale Robinson, transferred away from the program in the offseason. Frost and his coaching staff are not developing NFL talent. And the recruiting has completely fallen off. 247 Sports rated the Huskers’ 2022 recruiting class as last in the Big Ten. Frost became a hot name in the college football ranks when he oversaw Central Florida’s 13-0 season in 2017-18. Frost took over that program the year prior after the Golden Knights bottomed out with an 0-12 record. That 2016-17 season for UCF was fluky in that they lost a bunch of talent from the prior year — and then they got hit hard with injuries, bad luck, upset losses, close losses, and a retiring head coach midseason. Frost came to Orlando from Oregon where he was the offensive coordinator. With George O’Leary’s recruits, he was handed a great situation -- which he took full advantage of. Yet in Frost’s five seasons as a head coach, that 2017-18 campaign at Central Florida was the only team he coached a team with a winning record. I think coaches should get some benefit of the doubt from last year’s results given the challenges of COVID. But with the early returns now in for Nebraska this season, the program is lost under Frost’s guidance. Best of luck — Frank.

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Combating Losing Streaks: My Self-Audit Process

Saturday, Jul 31, 2021

Losing streaks are inevitable. If a bettor is making one to four bets a day on average (and more during football and college basketball season), then there are going to be some dry stretches over those 1000 or so tickets. The key to long-term betting success is not avoiding losing streaks as much as how to handle losing streak. First and foremost, do no harm. Don’t chase bad money with good money. Now is the time to remain ever-vigilant in maintaining your standards regarding what is a worthwhile situation for investment and what is not. Stay consistent. For me, if I am not handicapping well, it is because I have lost the balance between my qualitative analysis and my handicapping fundamentals. By qualitative analysis, I mean my understanding and appreciation of what is going on between the two teams in question. Do I have the right take on a team being undervalued or overvalued? Am I missing information regarding injuries? Are there changes in tactics that are impacting things? Have I fallen into the trap of accepting conventional wisdom?By handicapping fundamentals, I mean remaining sensitive to the betting situation independent of the particulars of the two teams in question. Am I investing in situations that I would otherwise draw red flags? Am I ignoring situations that I would otherwise jump on? I am betting on too many plays? Am I not getting enough action out there?When I am most successful, the decisions I am making on a daily basis take into account the specifics of the two teams in question and then balancing those thoughts with the handicapper situational perspective independent from the two teams in question. When the qualitative assessments and handicapper instincts are in unison, that should lead to strong plays. If those two perspectives are in conflict, I should be passing. When the picture is muddier, I should be weighing evidence and making decisions to play or pass. After losses, I conduct autopsies to discern if I made a judgment mistake. If the losses coincide with ignoring some of the handicapping fundamentals I have accrued over the years, then it is a pretty easy fix to get back to prioritizing those values. If the autopsy exposes that I did not know as much about the issues that would decide the game, then I need to get in the trenches and learn more about the teams. Sometimes that is simply a function of harder work. But sometimes this work requires the difficult decision that the sources I am leaning on are not making a winning difference. That requires me to dump sources of information in the search for better analysis to help inform my conclusions. More often than not, if I get stuck in a losing streak in a sport, it is because the research I conduct is not providing enough actionable information. As the years have gone by, I rely less and less on ESPN (TV and their print/web) sources to help inform my thoughts. 538.com has all but dropped off the planet for me. In an ideal world, I could read it all. In practice, I need to make choices in a 24-hour day. Making better choices as to where I get my supporting research is often the solution to losing streaks. But given all this, sometimes the best response to a losing streak is not change anything. Sometimes the breaks don’t go our way. It is called bad luck. It happens. Sometimes well-informed choices backed by sound handicapping fundamentals do not lead to a winning ticket. Successfully identifying those situations — and then not changing course — is the best route to long-term success. The most important quality to embrace when conducting a self-audit is brutal honesty. Perhaps the choices are bad? Or perhaps the knowledge of the teams is simply rudimentary. But if conducting an autopsy of past losses leads to the conclusion that the choice was sound and the handicapping of the situation was spot-on, then perhaps the best conclusion is to simply accept that we can’t win them all. And regarding the losing streak, this too shall pass.Best of luck — Frank.

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Someone, Please Kill the “Mike Budenholzer Won’t Make Adjustments” Narrative.

Saturday, Jul 31, 2021

OK, I’ll do it. The notion that Mike Budenholzer lacks the wherewithal to make adjustments on the fly or from game to game in the NBA playoffs was always a tired and lazy criticism. It is the American pastime to second-guess coaching decisions — it is the sports equivalent of the joy audience members have in ridiculing the bad singer on American Idol or the craven power-hungry losers on Survivor. The programming serves the self-satisfying ego of the viewer by offering a few fleeting moments of superiority. The same dynamic works in sports with sports radio, the 24-Hour Hot Take TV Industry, and much of the color commentary in-game focused on the mistakes made by the coaches and players. And when the drive to feel superior to someone on TV can combine with the beehive mentality of jumping on an establishing bandwagon for some good ole confirmation bias feedback loops, the makings of conventional wisdom form. It is not uncommon for this conventional wisdom to be flat wrong that is beside the point. Flattering the ego of the individual presenting the Hot Take is the medium and the message. I don’t know how good of a basketball coach Mike Budenholzer is. I am not qualified to assess his tactical decisions. I also lack the inside knowledge regarding what his realistic options were at hand when a chance in tactics was perhaps needed. But I did take careful notes of the adjustments he made in the 2021 postseason which ultimately led to his Milwaukee Bucks winning the NBA title. I’ll identify a few.(1) Played his best players for more minutes in the playoffs. A common criticism Budenholzer received during the NBA playoffs in the bubble last year was that he was not playing his Big Two of Giannis Antetokounmpo and Kris Middleton's higher minutes. In Games One-Three of the Eastern Conference Semifinals against Miami, Antetokounmpo averaged 37.9 minutes per game before getting injured in Game Four which completed the Heat’s four-game sweep. Middleton averaged 39.2 minutes per game in those four games. This critique is always troublesome for outsiders who lack the inside knowledge regarding how comfortable the player is in playing extended minutes. Interestingly, Budenholzer appeared to give Antetokounmpo the green light to take himself out of the game in this postseason. Perhaps that was seen as a necessity since Antetokoumpo was playing through injuries? Despite acquiring Jrue Holiday in the offseason to give his team a Big Three, Budenholzer did play those stars for longer periods in this postseason. Antetokounmpo averaged 40 minutes per game in the Brooklyn Nets series before average 39.8 minutes per game in the NBA Finals. Middleton averaged 42.5 minutes per game in the NBA Finals. Holiday averaged 41.7 minutes per game in the Finals. Predictably, I recall seeing some who criticized Budenholzer for not playing his stars enough last postseason now blame him for overworking his Big Three in these playoffs. Once the conclusion is determined (Budenholzer Bad!), the most important thing for some becomes confirming one’s prior assumptions. (2) Break the Giannis defensive wall by putting the ball in Middleton’s hands. Much was made of the defensive strategy that the Miami Heat deployed last year where they positioned three or four players into a wall-like formation to take away Antetokounmpo’s driving ability. In theory, the Bucks “adjustment” is simply for Antetokounmpo to drive-and-dish to an open shooter behind the arc to punish the tactic with 3-pointers — but the shooters need to make shots. It is hard to blame Budenholzer for shots not falling. However, this might be an offensive strategy that works better during the regular season rather than during the pressure of playoff basketball (see the James Harden Houston Rockets). Budenholzer’s adjustment in the playoffs this season was to take the ball out of Antetokounmpo’s hands as the primary ball-handler and let Middleton dictate the offense. Not only did Middleton thrive in this role with clutch baskets, but it allowed Antetokounmpo to crash the glass for second-chance scoring opportunities. (3) Pairing Bobby Portis with Antetokounmpo. Budenholzer did this early in the playoffs but got away from him in the Nets series since Portis was a liability on defense. But after falling behind 0-2 to the Suns in the NBA Finals, Coach Bud got back to getting Portis on the court with the Greek Freak. Portis was a three-point shooting threat that Phoenix had to respect. As opposed to when Brook Lopez or P.J. Tucker is on the court when Budenholzer could give Antetokounmpo stretches of the game where he was surrounded by four shooters to create more space for him to drive to the hole. When coaches like Ty Lue make elementary adjustments like this, they are lauded as geniuses. (4) Pick-and-roll defensive subtleties. It seemed like it was June of 2021 when many in the analytics community were introduced to the concept of drop coverage defense against pick-and-rolls. Rather than engage in a full-on switch to combat the offensive team’s pick, drop coverage has the switching defender accept the new defensive assignment but play off the ball. This move temporarily takes away a driving lane or a cut by the picker while giving time for a potential switch-back. But the drop does give space to the ball handler for an open jump shot. Chris Paul punished this tactic in the opening game of the Finals with his great mid-range game. It was a fascinating development to watch many in the analytics community calling the 2-point midrange shot the worst shot in basketball now blasting Budenholzer for a defensive tactic that lulled the opposition into taking this very shot. OK, whatever. Brook Lopez is an outstanding defensive player on drop coverage. Rather than completely abandoning this defense, the Bucks had Lopez just not drop back so much and play a step or two closer to the potential CP3 jumper to offer more resistance — and hand closer to the face. Paul was never as effective on these shots the rest of the series. (5) Deploy Holiday to start defending Paul in the backcourt. A question the Bucks’ brain trust had entering the NBA Finals regarded how to use Holiday as their best on-the-ball defender. Should he draw the assignment against Paul or Devin Booker? In Game One, Holiday defended Booker — and Paul had his big game. In Game Two, not only did Budenholzer switch assignments, but he had Holiday begin his defensive assault on CP3 as soon as he got the ball in the backcourt — forcing the veteran to exert more energy just to get into their half-court offense. Within three games of this tactic, Michael Wilbon was reduced to making excuses for his self-proclaimed best friend regarding a secret injury that we must not know about. These adjustments are just from my notes. I am sure there is more than those with a more sophisticated knowledge of the game appreciated. But coaches should not necessarily be judged on the adjustments they make. Sometimes the best tactical decision is to resist the urge to abandon ship on the strategies that have succeeded in the past. And every adjustment comes with a tradeoff. The Bucks led the league in defensive free throw rate in the regular season and the playoffs. That was not an accident. It was by design. Drop coverage on pick-and-rolls helps to lower foul rates since it is disincentives the player with the ball to drive the lane. Shooting midrange jump shots are less likely to draw fouls. And when your team is so dependent on Antetokounmpo, perhaps ensuring he does not get into foul trouble is a smart tactic? I don’t know if drop coverage on pick-and-rolls is better than switching with tight coverage or even not switching and fighting through the pick. I do know that if Wilbon or any of the other ankle-biters on the bandwagon want to criticize a tactic, they should at least engage the argument regarding why the tradeoff from the adjustment does not make things worse. Unfortunately, the notion that Budenholzer does not make adjustments will likely continue. Zombie narratives continue even after championships. But, those who continue to make the argument do serve a public good by telling on themselves. Best of luck — Frank.

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The Fundamental Flaw in Regular Season NHL Analytics When Applied to the Postseason.

Wednesday, Jun 30, 2021

The Colorado Avalanche were laptop darlings who put up the best five-on-five regular season analytics since that advanced data started being tracked closely in 2007-08. Despite being significant favorites to win the West Division Finals, they were upset by the Vegas Golden Knights in six games. Vegas was then upset in the Stanley Cup Semifinals against a Montreal Canadiens team that many mathematical projections considered the worst team in the playoffs. The Golden Knights were -5000 money line favorites to advance to the Stanley Cup Finals. The New York Islanders overperformed relative to the projections from the analytics’ models for the third straight season under head coach Barry Trotz by taking the Tampa Bay Lightning to seven games in their Stanley Cup Semifinals series. What gives? Are these outlier results? Or is there a fundamental flaw in the assumptions of the mathematical projections used to make predictions in the Stanley Cup playoffs? I think that is the case.Frankly, most of the methods I use to handicap the NHL in the regular season I throw out when handicapping in the playoffs. The goals and dynamic of the regular season seem too different than the competitive experience of the Stanley Cup playoffs. This is not just my observation from handicapping the NHL for 25 years — this is what NHL players and coaches say. The regular season is a grind with teams playing three or four times a week. Often the zeal from the players is on scoring goals and padding statistics since stats help with new contracts. Seeding for the playoffs is not nearly the priority that it is in other sports since home-ice advantage is less of a factor. The coaching edge in making the final shift change at home is important, but the roar of the crowd has less impact on the game, generally, because the game is so fast. Just getting into the playoffs healthy and rested is more important than seizing a higher seed. The Los Angeles Kings won the Stanley Cup in 2012 despite being seeded eighth in the Western Conference. None of the top seeds in the four divisions this year advanced to the Semifinals. Once the playoffs start, the zeal shifts from scoring goals to stopping goals. Players are much more willing to sacrifice their bodies to block shots. Star players take longer shifts. Benches are shortened. And the nature of a seven-game series completely changes the game-to-game dynamic. Players and coaches get deeper into their planning and preparation in stopping their opponents. Speed advantages begin to get neutralized. In the battle between offensive technique and defensive tactics, the defense tends to get the upper hand. The higher stakes of the playoffs create a new sense of urgency not felt in the regular season. Game management mistakes are more often game-changing plays. Finally, the referees are more likely to swallow their whistles and let the action continue. I have seen many NHL analytics experts complain loudly about the lack of penalties in this postseason. They might as well complain about the weather. It has been that way for decades. Every team that has made the playoffs in the last 50 years has sob stories. Keep crying (and losing), or adapt. There is a long list of NHL teams that were dynamic offensive teams in the regular season who then folded in the postseason. Tampa Bay had this problem before adjusting their style of play and making some subtle changes to their roster after getting swept in the first round of the 2019 playoffs to a defensive-minded Columbus team. That Lightning team also needed to learn to be better game-managers. Colorado made some critical mistakes in their series with Vegas. Rather than making the high-risk pass in the third period that often netted an additional goal, the Avalanche need to learn to not risk the costly turnover that might give their opponents a breakaway advantage. Scoring more is not as valuable as not risking getting exposed. When Tampa Bay finally learned that lesson, they won the Stanley Cup last year — and they appear to be on the verge of repeating as champions.The analytics folks are in a pickle as to how to adapt. Their modus operandi depends on the predictability of their data. It is difficult for them to concede the limitations of their regular season numbers, even if that happens to be the case. By the time the sample sizes become actionable in the postseason, the playoffs are almost over. Handicappers (like me) that also incorporate qualitative analysis can still find success (never more effective than in the 2017 playoffs where we ended on a 20-3 sides run with Pittsburgh’s repeat as Stanley Cup champions). Teams with veteran players with playoff experience that play defensive-first physical hockey with counter-attacking offensive tactics and elite goal-tending fit a profile that tends to perform better in the playoffs. That description seems to apply to the Montreal Canadiens and New York Islanders this postseason. Perhaps the NHL analytics community would find fertile ground in identifying team profiles that have success in the playoffs — and then make appropriate comparisons to new playoff teams. This was the interesting approach that Jordan Brenner and Peter Keating endeavored in their seminal 2015 article on Giant Killers in ESPN The Magazine that profiled the different templates of teams that tend to pull upsets in college basketball’s March Madness.Best of luck — Frank.

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Was Jon Rahm’s Extra-Motivation the Key to Winning the US Open?

Wednesday, Jun 30, 2021

I think it was George Carlin who had a comedy bit in his stand-up act that mocked the notion of someone putting on their “game face.” Cue Carlin trying out several goofy facial expressions mimicking the hypothetical professional athlete who is ready to get down to business to take their craft extra-seriously. Carlin was the best.I thought of this joke when handicapping the US Open and reading many of the assessments regarding Jon Rahm’s chances of winning the tournament. The Spaniard was returning to action for the first time since having to disqualify himself ten days prior after the third round of the Memorial Tournament where he held a dominant six-stroke lead before getting word that he tested positive for COVID. With Rahm out of the way, space was opened up for Patrick Cantlay to outduel Collin Morikawa to win Jack Nicklaus’ tournament at Muirfield Village. There was some commentary that Rahm should have been allowed to complete the final round by himself early the next day in place of having to be disqualified. That is absurd. Part of the professional challenge in winning a golf tournament is to handle the pressure of performing when winning or losing is on the line. Round Four of a golf tournament is not the SATs, to be completed whenever possible. Besides, letting Rahm play by himself to complete the tournament would have given him a competitive advantage after violating a tournament rule (don’t test positive for COVID). It was unfortunate, but so are many of the rules of professional golf. The rules should not change depending on how where the golfer is on the leader board at the time.I digress. With Rahm cleared from COVID quarantine just in time for the US Open at Torrey Pines, many observers picked Rahm to win the event because he would be particularly motivated to prove himself after being robbed of his chance to win the Memorial. There were plenty of reasons to favor Rahm to win the US Open — but thinking he retained “extra” motivation after his DQ two weeks earlier was not a good one. At all. Was Rahm not motivated to finally win his first major before losing out on his opportunity to win the Memorial in Round Four? Were his competitors that week less motivated to win a major championship because their season had yet to be interrupted by COVID (or anything else). Brooks Koepka? Bryson DeChambeau? Jordan Spieth? Really?And, look, I am a handicapper that tries to make assessments into relative differences in motivation. That level of qualitative analysis is one of the tools in my proverbial toolbox. The handicappers and forecasters that rely exclusively on quantitative analysis tend to dismiss motivation as a factor. I think there are plenty of times when there may be discrepancies between two sides regarding how much they want to win — even amongst professionals. To be specific, I suspect that the drive that Phil Mickelson had to win the US Open may not have been as strong as it was when he pulled off his historic victory at the PGA Championship the previous month. Don’t get me wrong: when Mickelson teed off on the first tee in the first round, he probably wanted to win just as much as Jon Rahm or Brooks Koepka did. But this gets to the broader point: I care about motivation when it translates into harder and longer work to prepare for a tournament or game. If the players on the University of Michigan football team are all preparing ten extra hours in the week preparing to play arch-rival Ohio State, that may finally translate into a victory again against the Buckeyes. I am not sure if Mickelson worked quite as hard to prepare for the US Open as he was to prepare for the PGA Championship. It is only natural to level off your work once you find the success one is seeking. For Rahm, being even more motivated to win the US Open after suffering his DQ at the Memorial would have translated into more time practicing his craft on the golf course. But Rahm was required to be in quarantine — so he could not put in the work that makes the difference vis-a-vis your peers when there is a difference in motivation. Maybe the physical and mental break from golf helped Rahm once the US Open started? Could be — but that is a different argument (and one I considered). There were plenty of good reasons to like Rahm to win the US Open. He ranked number one on the tour in Adjusted Scoring at the time. He was playing great golf, as evidenced by his dominant lead at the Memorial before his DQ. He had a great course history at Torrey Pines. But Rahm wanting to prove something after losing out his chance to win the Memorial was not one of them. He was robbed of the ability to translate that extra incentive into the tangible work that makes a difference once the competition starts. And I do not buy the notion that competitors care more about success once their event starts.I passed on Rahm to win the US Open, mostly because his price at +1000 was too low. It looked like an underlay bet to me. My Best Best was on Brooks Koepka who closed at +1600 to win — and he finished fourth. Koepka is on record admitting his focus tends to wane at non-majors — so there is no way I think he was less motivated to win his third US Open than Rahm was to finally win his first major championship. Rahm having something to prove at the US Open after getting DQed in his last start was an easy sound bite or sentence to write. Too easy, and not very smart. As if the DQ afforded Rahm the opportunity he finally needed to find his “game face.” Best of luck — Frank.

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Is It Time to Begin Fading the NBA Playoff Zig-Zag?

Monday, May 31, 2021

The Zig-Zag theory for handicapping the NBA playoffs has steadily gained in popularity over the last few years. The concept is appealing: bet on teams coming off a loss in the playoffs to cover the point spread. It is pretty easy to fill in the narrative to justify the strategy. Losing teams have more resolve to bounce back from a loss. Winning teams may have just a little less urgency to win again. And, at one point, teams coming off a playoff win in the series may become overvalued in the next game if the betting public becomes too enamored with the Recency Effect.But the point spread is the run — and the great equalizer. If the betting public begins to ignore the Recency Effect and begin banking on the Zig-Zag theory, the oddsmakers will adjust with worse lines on the team coming off a loss. Every half-point line moment matters as it decreases the win probability of that side covering the point spread. As more and more bettors employ the Zig-Zag approach, the value in the system will likely decline. I wonder if there will become a time where the betting value will become to fly the contrarian flag and bet against the zig-zag?The most important thing to consider when assessing past data is that it must be analyzed in relation to the point spread situation in question. Maybe a home favorite coming off a point spread loss by double-digits have covered the point spread in 28 of these last 32 situations — but if that home favorite is laying, say, 100 points, then they are not going to cover the point spread no matter who shiny the data mined angle is. Obviously, this example is exaggerated, but I do so to make this observation: there is a threshold as to when a past truth derived from empirical data will stop being a truth in the future given the changing expectations of the situation as demonstrated by the fluid point spread. Let’s look at how the Zig-Zag theory has done so far through four games in the first round of the NBA playoffs. Overall, teams coming off straight-up losses in the previous playoff game this season have then covered the point spread in 11 of the 24 games. Or, put another way, Zig-Zag is 11-13 ATS in Games Two, Three, and Four this postseason. Sample size is always a consideration, but zombie auto bet Zig-Zaggers have not made money in these playoffs. Let’s look at the game-by-game breakdowns. In Game Two, teams off a straight-up loss in Game One are 4-4 ATS. In Game Three, teams off a straight-up loss in Game Two are 3-5 ATS. In Game Four, teams off a straight-up loss in Game Three are 4-4 ATS. In general, I am highly skeptical of silver bullet evidence to inform a situation in which to invest. The fluidity of the market makes such evidence short-term, at best. My handicapping approach is to assess and then analyze a wealth of evidence utilizing several different methods and approaches. My Reports are long to communicate this investment. Zig-Zag is certainly something I look at. I find the theory carries more weight when it combines with other intangibles such as team trends that help to expose a personality of a team.I will continue to assess how the Zig-Zag theory is working as the NBA playoffs move forward. Sharp bettors should already be exerted caution when considering the system. Sometimes the best answer to a Zig is to just pass. The day may come sooner rather than later that the expectation of a Zag from the betting market may create value to keep on Zigging. Best of luck — Frank.

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NHL Playoff Over/Under Trends After the 1st Round

Monday, May 31, 2021

One of the data items I track is Over/Under results per each NHL game. I have found that there are certain games in a playoff series that are more likely to go Over the Total, and other games in a series that are likely to finish Under the Total. Following these trends helps to inform my decisions as to when to invest in a Total — and when to avoid investing in a Total I was leaning towards. These are not rules; it would be foolish to just zombie auto-bet the Over just because Game Threes in a playoff series have tended to be higher-scoring affairs. Data mining empirical trends like that — and then relying on them — is a good way to lose a bankroll. Instead, these data provide just another piece of evidence to consider when evaluating the case to play an Over, an Under, or to pass on the Totals situation. Game Ones had three Overs and five Unders in the opening round of the Stanley Cup playoffs. Game Twos had two Overs and six Unders in the first round.Game Threes had seven Overs and one under. Game Fours had four Overs and four Unders. Game Fives had two Overs and six Unders.Game Sixes had two Overs and three Unders. Game Sevens had one Over and one Under.These data offer a couple of surprises. The Game Two and Game Three numbers are of note. The sample sizes are small, so there is not much to conclude from just the first round. But this information continues to fine-tune the data I have been collecting. And trends may change over time. A thing to keep in mind with all NHL data so far this season is that every number has been produced from divisional contests. There has to be a game played out-of-division. I am interested in if the exclusive divisional schedules would result in more Unders or more Overs given the heightened familiarity between the teams. In the first round of the Stanley Cup playoffs, there were 21 Overs and 24 Unders. Let’s break that data down by division.North Division: four Overs, seven Unders. East Division: four Overs, seven Unders. Central Division: seven Overs, five Unders. West Division: six Overs, five Unders.I will keep looking at these trends in the second round of the playoffs. Assessing this data is just another tool in the toolbox.Best of luck — Frank.

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Aaron Rodgers and Nick Sirianni: Secret Soul Mates?

Friday, Apr 30, 2021

At first glance, it would not seem that Aaron Rodgers and Nick Sirianni would have much in common outside interest in scheming NFL plays on offense. Rodgers is a Super Bowl-winning quarterback destined for the Hall of Fame. Sirianni was the offensive coordinator for the Indianapolis Colts before flop sweating through his first press conference in a golf shirt as the new head coach for the Philadelphia Eagles. But both individuals have something in common: they have been involved in dramas that reflect the increasing trend of NFL franchises to treat the white-collar management as the superstars of the organization. This is a trend that started in Major League Baseball. General managers began taking their role to be more than just assembling the parts for their managers to manipulate. The movie Moneyball depicts this phenomenon with Brad Pitt’s Billy Beane frustrated with Philip Seymour Hoffman’s Art Howe reluctance to use the players the way in which he wanted. Beane was the hero in that story. He was adventurous rebel thinking outside the box as the underdog running a baseball team that could not extravagantly spend the money that rich teams were able to indulge. Howe was the curmudgeon locked in the old world. Jonah Hill played the role of Paul DePodesta who Beane hires to teach him the secret math taught in the Ivy Leagues that would revolutionize baseball. The secret sauce is dubbed “sabermetrics” in baseball while analyzing statistics in other sports has been given the esoteric label of “analytics.” As I have written elsewhere, the secret math offers capital-T Truth — and those who question it are small-minded dinosaurs. Well, at least that is the impression that some who dabble (and profit) in the sports analytics community tend to frame the issues. An embrace of a deeper appreciation of statistics in football was inevitable, but its growth in football is neatly symbolized by DePodesta leaving the Oakland A’s for the NFL and the Cleveland Browns. When Browns' general manager Sashi Brown traded away many of his veterans to acquire draft choices, it was as if DePodesta had discovered the concept of tanking games in ancient texts at the Harvard library. For many, the Browns’ Super Bowl was inevitable — and it was going to be clear who the heroes would be in that story. Analytics are being embraced by every sport. In the NBA, we see its influence in the Philadelphia 76ers “trust the process” mantra that led to their current superstar duo of Joel Embiid and Ben Simmons. General manager Daryl Morey was highly influenced by analytics is assembling his Houston Rockets teams that were allergic to any shot that was not at the rim or behind the arc. Morey hired a head coach in Mike D’Antoni who shared this offensive philosophy. Morey now happens to be the GM for the Sixers. The number of championship appearances for the Cleveland Browns, Philadelphia 76ers, and Houston Rockets remains zero. To be fair, there are teams across all the major sports that have invested in analytics to then win championships. The Houston Astros offer the best example in MLB, and the Boston Red Sox won the World Series with an analytics department even after they failed to woo Beane away from Oakland. But then again, it is not as if the use of statistics was invented by the Oakland A’s. Bill Belichick is shrewd with his ability to use statistics to help inform his decisions as head coach and general manager of the New England Patriots. But Belichick does not have a shiny Ivy League degree hanging on his office wall. It is harder to peddle the “Belichick” way on your new statistics website. This brings us back to Aaron Rodgers and Nick Sirianni. Rodgers is in a power struggle with Green Bay general manager Brian Gutekunst and his hand-picked head coach, Matt LaFleur. Remember that LaFleur was the offensive coordinator of the middling Tennessee Titans offense that was continually flailing away on 4th-and-1s in 2018-19. But LaFleur was buddies with Sean McVay, the new wunderkind who could solve personnel mismatches with the power of his intellect and a nearby whiteboard. Now granted, Gutekunst is no Harvard man. But even a graduate of the University of Wisconsin-La Crosse appreciates the power of pulling the strings from behind his office desk. He could hire LaFleur to “fix” Rodgers — and if the veteran quarterback doesn’t like it, then he would just get another quarterback that LaFleur would scheme-to-success using the new secret sauce. It has been reported that it was Gutekunst who made the unilateral decision to trade up in the first round last year to draft quarterback Jordan Love. Amidst whispers of Love struggling in practices (albeit, in a season impacted by COVID taking away the normal practice routines), LaFleur never elevated him above the third string. The Packers’ management has communicated that Rodgers is expendable. That motivated Rodgers’ rebuttal asking to be traded on the first day of the NFL draft. It was interesting to then observe Gutekunst choosing a defensive player in the first round, an area of need but not a choice that would placate Rodgers. In Philadelphia, general manager Howie Roseman along with highly engaged owner Jeffrey Lurie proved that their Super Bowl-winning head coach, Doug Pederson, was expendable when they fired him after the season. Did one of them set Pederson up by demanding he makes sure Nate Sudfeld got some reps at quarterback, despite the Eagles playing in a close game with Washington late in the season? Sure, Philadelphia was “trusting the process” by tanking, but management (and/or ownership) telling the head coach how to use his players comes straight out of Brad Pitt trading away the first baseman that Philip Seymour Hoffman was playing every day instead of Chris Pratt. It was later revealed that Roseman and Lurie would spend hours berating Pederson after games while demanding he defends the coaching decisions he made during the game. Roseman got his Law Degree at the University of Florida. Lurie was an academic with a doctorate before he finally followed in the footsteps of Michael Corleone to go into the family business to run the Hollywood movie company his grandfather founded. He produced Inside Man. Pederson was a career backup quarterback after a college career at UL-Monroe. Roseman and Lurie lure Nick Sirianni away from Indianapolis who was the offensive coordinator for Frank Reich, who was the offensive coordinator for the Eagles’ Super Bowl triumph. The 39-year-old looked over his head in his first press conference. He was nervous, which is not a crime. But it is also a characteristic of someone likely to be easily intimidated. Sirianni comes from a coaching family who began his coaching career after graduating from Mt. Union where he was a Division-III star. Perhaps it is that experience where he developed the rigor for competition that motivated his Rock-Paper-Scissors zoom duels with his new players? Sirianni may find success in Philadelphia, despite what seems to be a rocky start. What happens in August will matter far more than these initial events. He inherits a hot mess. But it seems evident that Roseman and Lurie hired someone who will take orders. Like in Major League Baseball where Ivy Leaguers have redefined the game to be a battle between strikeouts versus home runs because of The Math — the same Math that discovered that three points count more than two in basketball — the National Football League is slowly being taken over by the suits who know better. Because they have always known better. Just like the Enron guys. They were the smartest guys in the room too. How’s Enron doing lately?

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Expand the College Football Playoffs!

Friday, Apr 30, 2021

FBS commissioners are considering the expansion of the number of teams that would qualify for the college football playoffs. This is a good idea! The creation of a four-team playoff was considered by many to be a lean-and-mean compromise at the time. Too often there was at least another team that had a credible argument to be considered to be chosen by the Bowl Championship Series to be one of the two teams to play for the National Championship. Added a semifinal playoff round of four teams addressed that issue without creating a large bracket akin to March Madness. But the new system has instead entrenched a hierarchy of the have and the have-nots. In the eight years with a four-team playoff, only eleven college football programs have competed in the event. Only four programs have won a national championship. The structure is not working to create and maintain fan interest. The powers-that-be should expand the college football playoff to 12 teams. The top four seeds should get byes into the quarterfinals. Here’s why:(1) More football games with high stakes are good! For those of us in the sports gambling industry, this would be a boon. The more high-profile games, the better, at least until the product becomes oversaturated. With interest beginning to wane as the same three to four teams compete in the playoff, the problem is not the oversaturation of college football, but the predominance of the same teams getting the opportunity. (2) Upsets will happen! College football may be the only sporting event where the avenues to be rewarded for midseason improvement are foreclosed. The best NFL team over the last 20 years has been the New England Patriots. Bill Belichick has a long history of his teams playing poorly in September before finding the groove later in the season. Tampa Bay’s Super Bowl run this season was propelled by their playing their best football starting in December. Is midseason improvement simply limited to the professionals? Of course not. One of the fascinating elements of March Madness is observing the improvement in the play of college basketball teams benefiting from months of practice, coaching, and competition. How many college football teams peaked late in the season over the years that would have proven to be the best team in the nation if only given the opportunity? The notion that the Alabamas and Clemsons of the world would continue to blow teams out like we have seen recently in the semifinals is remarkably naive. Sure, some blowouts will happen, but so too will the upsets. Even if you do not think that the improving teams will challenge the traditional powerhouses when playing on a neutral field under the pressure of a single-elimination tournament, the law of probability indicates that it will be more difficult for favorites with an expected win probability of, say, 75% or better to win more games. (3) Changes to the current system can only disrupt the Alabama/Clemson dominance. Arguments that expanding the playoff will only help the programs currently thriving in the four-team playoff miss the point. Save for granting Alabama and Clemson an automatic bid into the semifinals, the current system could not benefit these two programs more, as is. As recent history attests, Alabama and Clemson can both lose a regular-season game and still be tapped to playing in the semifinals because they remain entrenched as the first two teams on the list of the next group of teams with the fewest losses. This gives Nick Saban and Dabo Swinney tremendous advantages. Expanding the playoff structure opens up these benefits to other programs. Don’t underestimate the impact this has on recruiting. If 12 teams make the playoff, Saban and Swinney can no longer pitch recruits that if they want the national spotlight of playing in the college football playoffs, then they better commit to their school. (4) Arguments against expansion misidentify the problem. Admittedly, there are concerns with asking college football players to play a longer season. More games mean a higher risk of injury. More games mean less time in the classroom. But these are arguments against a playoff, in general. It is disingenuous to only begin expressing concerns about injury and the sanctity of the student-athlete after a four-team playoff is in place. The genie is out of the bottle. Instead, the legitimate concerns about player safety and classroom time should be redirected to the higher ambition of finally getting the players paid for the efforts. Pay the players! This is a topic worthy of another discussion, but paying the players resolves most of the rationalizations to not expand the playoffs. Best of luck — Frank.

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Finding Hidden Value: CBB Maryland’s Home/Road Splits

Wednesday, Mar 31, 2021

Maryland’s head coach Mark Turgeon seemed to shift his tactics a bit after his team lost at Michigan, 87-63, on January 19th. There were 70 possessions in that game. The Terrapins did not see 70 possessions for the next 12 games lasting through the rest of the regular season. Turgeon slowed the pace of the games down particularly on the road. This change in approach helped to create some great betting opportunities later in the season. Let’s look at Maryland's first-round game in the NCAA Tournament against UConn.The Terrapins’ defensive prowess was underappreciated entering that game against the Huskies. While Maryland ranked just 95th in the nation in Adjusted Defensive Efficiency when playing at home at the time, they jumped to ninth-best in the nation in Adjusted Defensive Efficiency when playing on the road either on a neutral court or in true road games. But Turgeon was also seeing a decline in the offensive effectiveness of his team when playing on the road. At home, the Terrapins were 20th nationally in Adjusted Offensive Efficiency — but they fell to 96th in the nation in that metric on the road. They were scoring 62.1 PPG on the road on 42.1% shooting which was -6.7 PPG below their season average. Maryland seemed to be clearly improving their play on defense as the season moved on. They had held their last five opponents to just 63.4 PPG on 39.5% shooting even after Michigan made 51.7% of their shots against them on a neutral court in the Quarterfinals of the Big Ten tournament. They ranked 14th best in the nation in their last ten games in Adjusted Defensive Efficiency overall which is an improvement over their 25th ranking in that metric for the season going into the NCAA Tournament. Playing away from home where their offense declines but their defense improves offered an intriguing Under opportunity. But I do not handicap if I do not see complementary evidence from the other team involved in the game. In this instance, there was strong evidence from UConn that supported a play on the Under. The Huskies led the Big East by allowing only 64.6 PPG — and they held ten opponents to under 60 points. They have held their last five opponents to 39.9% shooting. And while they ranked 90th in the nation in Adjusted Defensive Efficiency when playing at home at the time, they improved to fifth-best in the nation in Adjusted Defensive Efficiency when playing on the road! And to put the icing on the cake, UConn was only making 40% of their shots on the road which resulted in 67.7 PPG which was -4.8 PPG below their season average. The Maryland/UConn Under was our 25* CBB NCAA Tournament First Round Total of the Year — and we were rewarded with a winning ticket after the Terrapins held the Huskies to just 54 points in their 63-54 victory that fell comfortably below the total that closed in the high 120s.This discovery about Maryland’s distinct play when playing away from College Park also played a significant role in the winning of our 25* CBB Big Ten Total of Year with the Under in the Terrapins’ 60-55 win at Northwestern on March 3rd. The Terrapins’ strong defensive play on the road also played a role in our backing them in the opening round of the Big Ten Tournament against a Michigan State team that played significantly better at home at the Breslin Center than they did on the road. Finding the hidden value in Maryland’s home/road defensive splits made the winning difference on several occasions.Best of luck for us — Frank. 

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Finding Hidden Value: CBB Arkansas’ Home/Road Defensive Splits

Wednesday, Mar 31, 2021

The NCAA Tournament Elite 8 matchup between Arkansas and Baylor presented great value on the Bears. The Razorbacks had once again skirted danger for the third straight game in the 2021 Big Dance by falling behind by double-digits against Oral Roberts before rallying for the win. I argued at the time that the Razorbacks cannot afford to do that for the fourth straight time against this Baylor team — the Bears will start hitting more 3s and the lead will be 20. Baylor did not win by 20 points — but they built a 29-11 cushion in the first half which helped them cover the 7.5-point spread with their 81-72 victory. It was not just the inconsistent play that worried me about Arkansas — it was their particular vulnerabilities they demonstrated that generated their lapses in play. The Razorbacks entered that game ranked 18th in the nation in Adjusted Net Efficiency when playing at home. However, they fell to 39th nationally in Adjusted Net Efficiency when on the road. Handicappers that only looked at overall Net Efficiency may have overlooked this discrepancy. Let’s put a microscope on this. Arkansas had the eighth-best Adjusted Defensive Efficiency at home, but they fell to 43rd in that metric on the road. Looking even closer, while the Razorbacks held their opponents to just 27.9% shooting from behind the arc at home, ranking 31st in the nation, their opponents make 39.1% of their 3-pointers on the road, ranking 317th nationally. I argued that Baylor was the wrong opponent for them playing outside Fayetteville. And while Arkansas wanted to force turnovers with their full-court press, the Bears’ four-guard lineup only turns the ball over in 16.2% of their possessions away from Waco, ranking 42nd nationally. The Razorbacks only made 32.6% of their 3-pointers away from home at the time, ranking 187th in the nation. Baylor made only 3 of 19 (15.8%) of their shots from behind the arc in their Sweet 16 game against Villanova despite leading the nation with a 41.5% clip from 3-point land at the time. Against this Arkansas team that had such disparate home/road splits regarding opponent 3-point shooting, the Bears converted 8 of their 15 (53.2%) of their 3-pointers to make the difference in their win and point spread cover. And we won our 25* CBB Elite 8 Game of the Year on Baylor! Finding the hidden value in Arkansas’ home/road defensive splits made the winning difference. Best of luck for us — Frank.

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The Curious Case of Brighton’s xG

Sunday, Feb 28, 2021

I wrote about the strengths and weaknesses of using expected goals (xG) analytics in handicapping soccer last summer. Since that time, Brighton and Hove Albion have become the poster child expressing the limitations of relying too heavily on these metrics. Bettors banking on the Regression Gods to finally help the Seagulls see more of their shots reach the back of the net likely find themselves in trouble now.As I wrote last summer: “Expected goals is a metric that determines a statistical probability on every scoring chance a team generates in a match. In this adventure of quantitative analysis, similar scoring situations are logged to determine a scoring probability from a deep data set in a way similar to measurements that predict the accuracy of an NBA shooter attempting a 22-foot corner 3-pointer. Shot attempts that have an empirical success rate of 35% or higher have been categorized as Big Chances. By reassessing a soccer match from the expected goals (xG) and expected goals allowed (xGA) given the activity and nature of all the shot attempts in a match. If xG analysis offers a better evaluation regarding how a team is playing, then it could provide a more precise way to measure subsequent action.”Brighton played at West Bromwich Albion yesterday (February 27th) as a -0.5 goal-line road favorite. For handicappers relying almost exclusively on xG, that match may have looked rather tasty. The Seagulls may have been only 4 points above relegation land 16th place in the EPL table, but their expected points generated from a dissection of their xG and xGA for the season projects them as the fifth-best team in the English Premier League. West Brom, on the other hand, was in 19th place in points and dead-last 20th place in xPTs. Easy win for Brighton, right? If those bettors then looked at the xG results after that match, they might have started shopping for their new beachfront property. The Seagulls generated 3.28 xG against the Baggies while surrendering just 0.73 xG. The most likely score given that activity is a comfortable 3-1 win for Brighton. The actual score? West Brom 1, Brighton 0. Perhaps that was yet another statistical aberration. Just like last week, when Brighton dominated Crystal Palace by a 3.03-0.27 mark in xG but lost, 2-1. Just like two matchweeks ago when the Seagulls outclasses Aston Villa by a 2.44-0.44 mark in xG but settled for a 0-0 draw. I like to refer to the gambler’s expectation of outlier numbers returning back to a normal a call to the Regression Gods. The Miami Dolphins’ defense was not going to continue to bail out Tua Tagovailoa’s meager passing days in his rookie season by forcing multiple turnovers week-after-week-after week. When called, the Regression Gods eventually arrive. But these Gods never promised to show up promptly — and we need to keep our bankroll for when they finally make their triumphant return in the pursuit of justice. Sometimes these underlying numbers are not simply outliers due for regression. Sometimes these numbers are descriptive. To paraphrase former NFL head coach Dennis Green, sometimes the numbers “are we thought they were!” (“and we let ‘em off the hook!”). Perhaps Brighton has scored only 27 goals despite their xG projecting that the typical team typical players would score 37.85 goals precisely because the Seagulls are a roster consisting of below-average players! As I wrote in the summer: “Expected goals attempt to determine the most likely outcomes. But not all outcomes are created equal. Lionel Messi is going to score more goals than Glenn Murray dribbling up the left-wing and talking a shot from 30 yards out.” Well, Aaron Maupay may have replaced Murray as the Brighton striker this season — but he is still no Messi. Don’t get me wrong, I love xG — and incorporating expected goals analysis has improved by handicapping in soccer and hockey (where similar principles apply). We just should not become zombies to these numbers — it will drive us to bankruptcy. You wanna be an analytics fundamentalist and exclusively following the betting advice at Football Outsiders when betting the NFL? Kiss your bankroll goodbye in about a month. The most successful handicapping incorporates a variety of tools in the proverbial toolbox. A final tip regarding xG: use these numbers to illustrate the prospective floor and ceiling regarding a team’s potential. Brighton’s xG promise did pay off on February 3rd of this month when they upset Liverpool by a 1-0 score. They won the xG battle by a 1.32-0.97 margin — so this was not a fluky victory. Perhaps one lesson regarding the handicapping application of xG is this: underperforming teams in xG make dangerous underdogs but unreliability favorites. Best of luck — Frank.

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Ya Think Iowa Plays Bad Defense? Check Out Ohio State!

Sunday, Feb 28, 2021

Iowa may have the National Player of the Year in college basketball this season in Luka Garza with the senior All-American going into the last day of February leading the nation with a 24.3 Points-Per-Game scoring average. He leads a Hawkeyes’ attack that makes 40.4% of their 3-pointers, good for fourth-best in the nation. Fran McCaffrey’s team also assists on 63.5% of their made baskets with this exquisite ball movement ranking seventh-best nationally. For a team that scores 85.2 Points-Per-Game while ranking second in the nation in Adjusted Offensive Efficiency, perhaps it is these elite numbers that draw attention to their meager numbers on the other end of the court. Iowa is just 59th in the nation in Adjusted Defensive Efficiency. Their opponents have an effective field goal percentage of 48.3% against them, ranking 104th nationally. The Hawkeyes do not attempt to force turnovers with their opponents only losing possession 16.2% of the time before a shot, 318th lowest nationally. And Iowa’s opponents nail 34.9% of their shots from behind the arc, 239th in the nation. These opponents are also taking 40.0% of their shots from downtown, with that mark being the 260th highest in the country. Perhaps the elite shooting Iowa brings to games is compelling their opponents to take more 3s to keep up? Maybe. But Iowa is clearly much better on offense with their liabilities on defense a red flag regarding their NCAA Tournament potential to make a deep run. The above seems to be fair criticism and assessment of the 2020-21 college basketball team entering March. But why have fellow Big Ten and nationally-ranked peers in Ohio State escaped similar scrutiny? The Buckeyes began the week as the number four ranked team in the nation and the de-facto fourth number one season in the NCAA Tournament — even after losing to third-ranked Michigan on February 21st. Sure, there is no shame in losing to this Wolverines team that is tearing up the Big Ten while only losing once all season even after a three-week COVID pause in the middle of the Big Ten season. Michigan scored 92 points against them while generating 1.37 Points-Per-Possession. Yet, the Wolverines got the credit and the Buckeyes got a pass since it was considered by many to be the best basketball game of the season. However, Ohio State entered their February 28th showdown with Iowa with worse defensive numbers across-the-board. The Buckeyes rank 81st in the nation in Adjusted Defensive Efficiency along with eighth in the Big Ten in that metric (just behind Iowa in the conference). Opponents have an opponent’s effective field goal percentage of 49.1%, ranking 136th nationally. Like the Hawkeyes, Ohio State does not attempt to force many turnovers with their opponents only coughing it up in 15.6% of their possessions, 328th nationally. The Buckeyes do perform a bit better than Iowa in a 3-point defense. Their opponents make 34.4% of their 3-pointers, 215th nationally, while taking 38.6% of their shots from downtown, the 215th lowest rate. Generally, the analytics folks consider 3-point percentage defense to be a function of luck but limiting 3-point attempts more a function of skill. Maybe … the Milwaukee Bucks’ Mike Budenholzer would likely quarrel with that diagnosis since his system tries to lull opponents into taking bad 3-point shots. Jim Boeheim’s 2-3 zone defense at Syracuse attempts to accomplish the same task. Needless to say, Ohio State’s defense appears on paper to be performing worse than the Iowa defense. And at least the Hawkeyes thrive in one area: they do a good job of defending inside the arc. Their opponents are along only 45.6 % of their 2-point shots, 38th best in the nation. The Buckeyes’ opponents are making 47.5% of their shots inside the arc which ranks a respectable 97th in the nation. But that number declines to a 50.1% clip in Big Ten play, good for 9th best, while Iowa still holds their conference foes to 46.2% shooting of their 2s in conference play, ranking third best.These numbers helped to set the stage for their clash earlier today where Iowa upset Ohio State in Columbus, 73-57, as a 3-point underdog. The Buckeyes did little to stop the Hawkeyes scoring attack. Iowa shot 47% from the field while nailing 10 of 24 (42%) of their 3-pointers. Iowa scored at a healthy 1.11 Points-Per-Possession clip which was not far below their Adjusted Offensive Efficiency projected rate of 1.249. However, Ohio State made only 5 of their 17 (29%) 3-point attempts en route to a 45% shooting performance. The Buckeyes scored at just a 0.86 Points-Per-Possession clip which was well below their projected Adjusted Offensive Efficiency of 1.226 per possession. Perhaps Ohio State just had a cold night shooting? Or perhaps the Iowa defense is steadily improving while the Buckeyes’ defense gets a pass due to their top-four ranking? During the Hawkeyes’ recent four-game winning streak, before losing at Michigan on Thursday, they had not allowed more than 68 points and 1.02 Points-Per-Possession during that stretch before the Wolverines scored 79 points at a 1.18 PPP rate. Now after their performance against the third-most efficient offense in the nation in the Buckeyes, Iowa has held five of their last six opponents below 69 points and 1.02 PPG. The Hawkeyes’ defense may be their Achilles’ heel in the Big Dance later this month. But the concerns they have on defense pale in comparison to the issues Chris Holtmann has with his Ohio State team right now.Best of luck — Frank.

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Early Season 76ers' Data with/without Joel Embiid

Sunday, Jan 31, 2021

In handicapping the Philadelphia/Minnesota contest in the NBA on Friday (January 28th), I was looking closely at playing the Over until discovering that Joel Embiid was questionable with a sore back. The possibility that the Sixers would be without Embiid prompting to investigate how they have performed with their star center so far this season.Embiid has not played in four of their first 19 games going into that contest on the road against the Timberwolves. Philadelphia lost all four games. On December 27th, they lost at Cleveland by a 118-94 score. On January 9th, they lost at home to Denver, 115-103. On January 16th, the Sixers lost at Memphis by a 106-104 score. Then on January 25th, they lost at Denver, 119-104. The average score of those losses was 115-101.3. The 76ers have a field goal percentage of 42.5% in those four games with Embiid not playing while allowing their opponents to make 49.0% of their shots.In comparison, Philadelphia averages 116.7 PPG while making 50.7% of their shots in the 15 games Embiid played this season going into the weekend. That translates into +15.4 more PPG and a net 8.2% improvement in their shooting. On defense, the Sixers have held their 15 opponents when Embiid played to 109.4 PPG on 43.2% shooting from the field. While Philadelphia allows 8.1 more PPG without Embiid this season, opponents are shooting 5.8% better from the field. In regards to my potential Over play, the Sixers averaged 226.1 combined PPG in their 14 games with Embiid as opposed to them averaging 216.1 combined PPG in the four games they played without Embiid. So far, the loss of Embiid’s presence on defense has been overwhelmed by the loss of his offensive skills — although analyzing the possible slowing of the tempo without Embiid deserves consideration. I passed on the Over. Embiid took part in the shootaround and later played 27 minutes in Philly’s 118-94 victory that finished well below the 225 total. Fortunately, I avoided losing money on the Over bet I was considering. But the data regarding how Philly plays without Embiid could be valuable moving forward.The 76ers gave Embiid the night off on Sunday to end the month in their game at Indiana. Philadelphia rallied from a 95-82 deficit entering the fourth quarter to defeat the Pacers, 119-110. Analyzing how and why the Sixers scored 37 points in the fourth quarter while limiting Indiana to only 15 points in the final 12 minutes will be interesting. Of initial note is that the 76ers made 45 of their 92 shots for a 48.9% shooting percentage which is well above the 42.5% clip they had shot without Embiid in their previous four games. Yet they also allowed the Pacers to nail 41 of their 84 shots for a 48.8% clip which remains almost identical to the opponent’s field goal percentage of 49.0% they had without Embiid in their previous four games. Perhaps the decline in the Sixers’ defense will continue in the games Embiid does not play this season? That would make sense. Was Philly’s offensive effort against Indiana (without Embiid) an aberration? Or was their big fourth-quarter shooting effort the result of the Regression Gods finally making an appearance for this team that simply been underachieving how they should be playing on offense even without Embiid? This will be interesting to continue to track. Best of luck for us — Frank.

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Using Shots-Gained Data to Handicap Golf

Sunday, Jan 31, 2021

When the PGA started using ShotLink data to provide more information for its golf events, it provided a remarkable resource for bettors. ShotLink uses proprietary data accumulated from cameras and lasers that track information each shot a golfer takes per round. I have written previously about the expected goals metric used in soccer. This data works similarly. ShotLink collects the data for every golfer from every one of his or her strokes during an event. This information can provide a point of comparison than for individual golfers. The concept of “strokes-gained” represents how an individual golfer's performance compared to the average from the field. This ShotLink data facilitates the statistical breakdown of each aspect of a golfer’s skill set. As an example, this information can be used to compare how a golfer performs 200 yards out from the pin versus the rest of the field. More generally, this data helps to create six different categories to help analyze a golfer’s strengths and weaknesses. Shots-Gained: Off-the-Tee measures a golfer’s first shot proficiency. Shots-Gained: Approach-the-Green measures a golfer’s ability to reach the green after their first shot. Shots-Gained: Around-the-Green measures a golfer’s pitching and sand wedge play. Shots-Gained: Putting measures how a golfer performs on the greens. Shots-Gained: Tee-to-Green measures the performance of a golfer off the tee to get to his or her putter.This collected data can be quite helpful in handicapping the field for PGA tournaments. When assessing the field for the PGA Farmers Insurance Open earlier this week, I gave considerable weight to the following statistics: Shots-Gained: Approach-the-Green, Par-4 Scoring, Bogey-Avoidance, Driving Distance. I did this given the nature of the South Course at Torrey Pines where three of the four rounds would be played this weekend. Past winners have excelled in those statistical categories. The South Course is a beast at 7465 yards with Poa Annua greens. The metrics I used to handicap this event given course history include Shots-Gained: Approach-the-Green, Par-4 Scoring, Bogey-Avoidance, Driving Distance. This event was graded as the fourth-most difficult on the PGA Tour last season with the average professional finishing +0.534 strokes above par.These considerations led me to choose Patrick Reed as my Top Overlay Bet to win this tournament with him having +2800 odds at BetOnline. Reed ranked third in Shots-Gained: Birdies or Better so far for the 2020-21 season. He finished last season 11th on the tour in Shots-Gained: Total. Reed’s short game is one of the best in the world. He is eighth in the field in Bogey-Avoidance over his last six months. He ranked 32nd last year in Par-5 Scoring. He finished the ’19-20 campaign 26th on the tour in Shots-Gained: Tee-to-Green. Reed was sixth on the tour in Par-4 Scoring. He is also great with the blade. Reed was fifth last year in Putting: Birdies or Better and 10th on the tour in Shots-Gained: Putting. Reed rewarded this work by winning the 2021 PGA Farmers Insurance Open by five strokes with his 14 under par. On to the PGA Waste Management Open, next week where analyzing the statistics that past winners have shared can help identify which of the professionals in next week’s field offers value.Best of luck for us — Frank.

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Betting Underdogs: Take the Points or Bet the Moneyline?

Thursday, Dec 31, 2020

When betting on an underdog, should you bet the moneyline with the expectation of the upset occurring or should you take the points for the value of the insurance of your team losing but staying inside the number? While there is a lower probability of the underdog winning the game, the oddsmakers account for this with progressively better payouts for those dogs that do pull the upset. In a previous article, I argued that thinking about why you are making the sports bet can help guide the answer to questions like this. I identified three reasons to bet on sports: (1) To Have Fun; (2) To Make Money; (3) To Prove You are Right. I strongly approve of the first two reasons — but I am very worried about those motivated by the bettors looking for validation to prove something from their financial investment.In this case, if you are betting simply to have fun, then the answer is simple: bet in a way that will maximize your enjoyment. Do you want the comfort of some insurance by taking the points? Then take the points! Do you just want to see if your underdog can pull the upset? Then bet the moneyline. If fun is your motivation, then there is not a wrong answer.There is a wrong answer if your goal in betting is to make money. However, it can very tricky to determine the correct answer regarding which bet will be more profitable. The bettor needs to accurately assess the long-term odds and payouts from the situation. Let's look at two examples. In Week 17 in the NFL, bettors have the option at BetOnline at the moment of taking the New York Giants as a +1 point underdog against the Dallas Cowboys at a -110 price or as moneyline underdog priced at +120. Let’s use $100 as the baseline bet. Moneyline bettors save $10 per bet if investing $100 as compared to point spread bettors investing $110 to win $100. However, what happens to the moneyline bettor if Dallas wins by a 17-16 score? Not only do they lose their $100 bet, but they also missed out on pushing the bet from which the point spread bettor benefited. For the moneyline bettor to be profitable in comparison to the point spread bettor, the final score would have to not end at +1 for the dog less than 20% of the time. The moneyline bettor is adding an extra $20 per bet versus the point spread bettor. However, it takes five straight winning bets to neutralize the dog losing by 1 point just once. Let’s look at the Chicago Bears getting +5 points versus the Green Bay Packers on Sunday. BetOnline offers the Bears at the moneyline price of +195. Using $100 as the baseline, moneyline bettors save $10 per loss versus the Chicago backers investing $110 to win $100 taking the +5 points. Out of ten bets, let's say the Bears lose by at least 6 points five times. The point spread bettor is down $50 dollars to the moneyline bettor. But if Chicago covers the point spread the other five times but only pulls the upset twice, the point spread bettor is better off. The point spread bettor wins $500 from those five times the Bears cover. Subtract the $50 in juice the point spread better invested at $110 that moneyline bettor avoided with $100 bets. That is a net of $450. The moneyline bettor wins $390 when Chicago pulls the upset twice. But they still lose the three other $100 bets when the Bears cover the point spread without pulling the upset. Their $90 is dwarfed by the point spread bettor’s $450 for a net loss in all ten bets of -$360. Not even a third Bears upset makes up for the net discrepancy since the $585 in winning tickets get reduced by $200 in the two Chicago point spread covers where they did not win the game as well. It would take two more Bears’ upset victories to turn the tables with the moneyline bettor generating $780 from those four winning tickets minus only one $100 losing ticket where Chicago covered the point spread for a net of $680 and a nice $230 profit over the point spread bettor.Got all that? It gets complicated. And the complexity is magnified because even the most technical sports gambler and handicapper are reduced to making estimated guesses regarding the probability of an upset. At least poker players have the benefit of more precise math when making decisions regarding implied pot odds. Frankly, if a sports bettor thinks they have something close to precise odds on the likelihood of an upset (versus a point spread cover), I would be very skeptical of that person. This sounds like someone trying to prove they are right about the dog being good (or the favorite being bad). I would not trust their math. One of the reasons I do not like the motivation to “prove oneself right” as the reason to make a sports bet is that emotions are involved. I consider emotions as a threat to making sound decisions. This is the reason I always prefer taking the point spread with underdog bets rather than taking the dog with the moneyline. I can live with not making even more money on my underdog bets. However, I am much more likely to go tilt if my underdog loses the game outright on a bad beat or buzzer-beater. I want to avoid negative emotions. And winning feels much better than losing. I will take more winning tickets even if some of them are not as rewarding as the occasional underdog winning outright. Winning promotes momentum and good cheer — and those are emotional conditions that help lead to better decisions for the next bet. For me, that is the route for more fun and to make more money.Best of luck for us — Frank.

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Why Bet on Sports? Two Good Reasons (and Exposing One Bad One).

Thursday, Dec 31, 2020

If winning a sports bet was easy, then everybody would be doing it. It is a great feeling to “love” a situation because of an insight you have on a particular game -- and then get validated by being right with the bonus of being rewarded by doubling your money. Unfortunately, easy wins are not the norm for those engaged in the long haul. When a gambler or handicapper goes on the hunt for wins on the daily sports card, the games available combined with the betting options available can seem overwhelming. Should I take the money-line instead of the point spread? What about parlaying those two plays together? Maybe teasing those two plays together would be better? I have found that answering some fundamental questions helps guide my betting decisions on a day-to-day basis. A most basic query for sports gamblers is this: Why are you betting on sports? This sentiment may sound like Jerry Maguire after eating a bad piece of cold pizza in the middle of the night. Yet finding an honest answer to this question can be quite helpful in guiding what game you bet on, what types of bets you make, and how much money you invest. I think there are three reasons to bet on sports. These reasons can overlap — but identifying what is most important for your motivation to make a bet can be quite helpful. The first two reasons below are good ones. I would be very cautious if you are motivated primarily by the third reason. (1) Bet on sports to have fun. This is a great reason. Hopefully, this means that any potential losses are manageable financially and not soul-crushing emotionally. Recreational betting to increase the enjoyment of watching a game on television or following the ESPN sports ticket can really get the dopamine firing away.(2) Bet on sports to make money. This is another great reason. However, accruing profits often means eschewing the fun-factor. It may require avoiding action on Monday Night Football. It also likely means that you should avoid the teasers, parlays, and other novelty bets that (generally) are offering less betting value than a simple straight against-the-spread bet.(3) Bet on sports to prove you are right. This is the dangerous motivation. For starters, making a prediction and then being right about does not require a financial investment. Scream away about it on Twitter or at the bar (then again, please don't do either of those because no one cares). The problem with the bettor looking to prove themselves right is that they will never find the validation they are searching for from a winning ticket. That bettor will likely continue to chase bets in the futile search to prove themselves to the world (or to themselves). The bets contain an emotional component. If you are going to bet with emotion, you should only do it for fun. A gambler betting on emotion is a gambler poised to lose. In future articles, I will get specific in how my “Mission Statement” regarding “why to bet on sports” guides me to make certain bets and avoid other types of bets. Best of luck for us — Frank.

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How Many QB Hits Are Too Many? Assessing Joe Burrow's Season-Ending Injury

Monday, Nov 30, 2020

When Joe Burrow suffered his season-ending in Week 11 of the NFL season with a gruesome leg injury, many observers thought that this outcome was inevitable given the punishment the rookie was consistently facing. In hindsight, the statistics are staggering regarding how the Cincinnati Bengals treated their franchise quarterback. Burrow endured his injury in the third quarter against a Washington team with one of the best pass rushes in the NFL. He had already attempted 34 passes at that point of the game, while being on track to approach 50 throws in the game. These efforts were all for a team that was 2-6-1 at the time, and far out of the AFC playoff race. Burrow’s rookie season ended with him attempting 404 passes in his 8 1/2 games played. But it is not just his pass attempts that is the concern. Burrow was playing behind a suspect offensive line that made a season-ending injury a significant risk. Burrow was sacked 32 times. However, sacks are an insufficient measurement of the damage he was absorbing. Burrow also took 53 hits in the pocket. When then adding the 34 rushing attempts he made where he was tackled, the result is that Burrow had been subject to 125 significant hits from opposing defenders in just over half a season. Is approaching 250 hits a sustainable number for a quarterback to stay healthy?There was intriguing research done on the toll of high-usage in bell-cow running backs in the early 2000s. Aaron Schatz of Football Outsiders dubbed the phrase “the Curse of 370” when detailing the tendency for running backs who had 370 touches from rushing attempts and receptions had on the future productivity (https://www.footballoutsiders.com/stat-analysis/2004/ricky-williams-retires). I find it surprising that similar research has not been undertaken to attempt to identify if there is a correlation between hits on the quarterback and injury. To be fair, I may be unaware of such research. Such work is not being addressed in the higher-profile pieces I have read arguing for passing the football in seemingly every instance despite the ancillary risk this strategy has in putting the quarterback’s health at risk. In researching this article, I did come across a promising fantasy football site that does attempt to measure this data: https://sportsinjurypredictor.com. However, I would be interested in research that attempted to determine that theoretical magic number regarding the number of hits a quarterback endures before the risk of injury seems to significantly rise. Jamey Eisenberg made some conclusions regarding running backs for his CBS Fantasy Football work in 2014 that illuminates how similar work could be undertaken regarding hits on the quarterback: “We went back and looked at the past 10 years for running backs who had 400 touches in a season, including the playoffs, and found 27 occasions where it happened for 17 different running backs. Of those 27 times, only five -- Edgerrin James (2004), LaDainian Tomlinson (2005), James again (2005), Adrian Peterson (2009) and Ray Rice (2011) -- produced an increase in Fantasy points the following season, and you can see all the data in our chart below. The 22 other examples where a running back hit 400 touches over that span showed varying results -- all negative. Two running backs -- Tiki Barber and Ricky Williams -- retired following consecutive seasons with 400 touches. And nine times a running back suffered an injury -- Arian Foster (2013), Peterson (2013), Michael Turner (2009), Steven Jackson (2007), Larry Johnson (2007), Shaun Alexander (2006), Clinton Portis (2006), Curtis Martin (2005) and Jamal Lewis (2004) -- that caused him to miss games following a 400-touch campaign. Some of those injuries could be attributed to the heavy workload the year before.” (https://www.cbssports.com/fantasy/football/news/offseason-extra-the-year-after-400-plus-touches/)Identifying a theoretical number of hits absorbed where the risks of injury significantly increased for a quarterback would be fascinating. It would certainly better inform the debates regarding offensive run versus pass strategy. And this level of scholarship might have compelled the Bengals to run the ball a bit more to save their star rookie quarterback from sustaining an injury that may put his 2021-22 season into jeopardy. Best of luck for us — Frank.

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"Let Russ Cook?" -- Confirming Some Priors From Last Month

Monday, Nov 30, 2020

I wrote last month about some of the negative consequences to the “Let Russ Cook” mantra coming from many NFL observers and Russell Wilson fans regarding the Seattle Seahawks opened up their passing game in the first half of their games. The Seahawks went on a two-game losing streak in the middle of November with losses against Buffalo and the Los Angeles Rams, which were Wilson’s two worst games of the season. Wilson was responsible for seven turnovers himself, including four interceptions. The conventional wisdom was that the coaching staff was asking him to do too much. Head coach Pete Carroll appears to have taken a step back from the “Let Russ Cook” philosophy in their 28-21 victory on Thursday Night Football on November 19th. For the second time all season, Seattle ran the ball more than they passed — they had 31 rush attempts for 165 yards, with Wilson completing 23 of his 28 pass attempts. He did not commit a turnover. The commitment to running the ball also helped them control possession of 35:07 minutes of that game, which kept Kyler Murray off the field. Arizona managed only 314 total yards in what was the fewest yards the Seattle defense had allowed all season. Those 314 yards were also the second-lowest mark that the Cardinals had generated in a game up to that point of the season. That performance is evidence of two of the benefits of running the football that too many in the football analytics community fail to appreciate when critiquing the “establish the run” mentality. First, running the football lowers the propensity of the quarterback turning the ball over. Fewer pass attempts are fewer opportunities to throw interceptions. And the quarterback will fumble the ball less if the ball is not in this hand. Of course, the player with the football can still turn the ball over with a fumble mistake, but it would be interesting to study if quarterbacks in the pocket are more susceptible to a fumble than running backs. Running backs expect to be hit while quarterbacks focused on passing the ball are vulnerable to blindside hits. Second, running the football burns time off the clock, which can lead to fewer offensive possessions from the opposing offense. Murray (or Deshaun Watson, et al) can not score from the sidelines. And defensive players who asked to play fewer snaps retain more energy at the end of the game. Some defensive coaches make the case that defensive players only have about 50 plays in them before their productivity begins to decline. That would be another intriguing area to study. Seattle goes into their Monday Night Football game at Philadelphia to conclude Week Twelve of the season with 33 sacks on the quarterback. That mark ranks 30th in the league. The Seahawks are also 31st in the NFL in Adjusted Sack Rate on offense. With quarterbacks like Joe Burrow out the year with season-ending injuries, asking Russ to cook a little less may also be the best way he can still be in the kitchen come playoff time. Best of luck for us — Frank.

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Go For It on 4th Down? We Need Better Analytics!

Saturday, Oct 31, 2020

When Mike McCarthy was auditioning for a new head coaching gig after being let go by the Green Bay Packers, he made it known that he spent his year away from the game camped out in his basement lair studying tape and immersing himself in “analytics.” After Jerry Jones hired him as his next head coach of the Dallas Cowboys, McCarthy made it seem that he had added the new-age analytics to his arsenal of coaching weapons. This new-found knowledge was tested right away in his first game as coach of the Cowboys. McCarthy found his team trailing by a 20-17 score in the fourth quarter. When the Dallas offense stalled at 4th-and-3 on the Los Rams’ 13-yard line, McCarthy bypassed attempting the 30-yard field goal since the “analytics” on 4th down attempts apparently provided a one-size-fits-all answer that required going for the 1st down to keep a potential touchdown drive alive. Unfortunately for the Cowboys faithful, Dallas failed to convert the 1st down and eventually lost the game by the same 20-17 score. I'm agnostic as to whether or not Mike McCarthy made the right or wrong call going for it at 4th-and-3 rather than kick the game-tying FG. However, the litany of defenses for his decision exposed one of the most flawed applications of analytics in football. Attempting to apply the "historical" probability odds of the success-rate on 4th-and-3 (or any other 4th down situation) to the Cowboys' specific chances in that spot represents deductive logic run amok.Here a just a few intangibles that would impact Dallas' success rate at that moment: (1) their field position; (2) the moment in the game; (3) the quality of the opponent’s defense; (3) their credible 3-yard rush play options; (4) their credible 3-yard pass play options; (5) the injury status/health of key offensive players. Each one of these considerations either impacts the specific success rate of the Cowboys’ 4th down play at that moment or contextualizes the risk calculus regarding the ramifications of the probability matrix if they settled for the field goal attempt. These intangibles expose the need for more precise data to identify qualitative factors that contextualize the "actual" probability. Head coaches conduct this additional level of analysis. And this inductive logic is even considered conventional wisdom in other situations! What are the "NFL history" odds for the probability of making a 45-yard field goal? 60%? Imagine that argument trotted out in a situation for a kicker who was struggling through out that very game in making chip-shot field goals (as was the case the next night for Monday Night Football when the Tennessee's place kicker Stephen Gostowksi made a game-winning field goal after missing kicks earlier in the game)?If the NFL history for success rate on 4th-and-3 is, say, 51% (guessing), that does not mean the Cowboys' had that same probability in that specific (statistical) moment. Maybe it was higher!It is indicative of football analytics still being in its infancy stage that this deductive logic is advanced so heavily. Imagine this argument being made after a hypothetical World Series moment: "Stolen base success rate is 55% -- so take your chances with (the relatively slow and non-base stealer) Cody Bellinger stealing 2nd (with two outs)!"Football coaches may, in fact, be better served by being more aggressive on 4th down. Citing general league-wide data that is even attempting to get more specific to being analogous to the situation at hand is flawed. Best of luck for us — Frank.

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Problems in the Kitchen when you “Let Russ Cook!”

Saturday, Oct 31, 2020

Seattle Seahawks fans, as well as the “you already lost if you did not pass on first down” football analytics crowd, have been vocal with their claims regarding how good that team would be if they passed the ball more in past seasons. This sentiment has evolved into the catchphrase “Let Russ Cook.”Yet when these critics call on the “old-fashioned” Seahawks' offense to run less and pass more (as offensive coordinator Brian Schottenheimer did in the first two games of the 2018 season before head coach Pete Carroll intervened), they typically fail to address Carroll's reasons for wanting to run the ball more. The direct and indirect benefits of running the football are under-appreciated by the football analytics community. Their arguments would strengthen if they better engaged with the rationale of (Super Bowl-winning) head coaches who find subtle advantages in running the football that transcends the yards-per-carry metric.First, “Russ can't cook” if Russ is on the sidelines with an injury. Carroll is very cognizant of the number of hits made on the quarterback.  He has this number tracked by his coaching staff. Carroll speaks of "not taking the sugar" when it comes to the short-term allure of relying on Wilson to make yet another pass. Every drop back risks another quarterback hit. Have there been studies in the analytic community regarding the correlation between the number of QB hits and injuries? I have not seen any (and I pay attention). These stats are not cited in the "you already lost if you ran on first down" genre of analytics.I would be surprised if this area was not being studied by internal analytics departments. Is there is a threshold where QB hits correlate with a higher risk of injury (like the 400 carry threshold pointing to RB regression the next season)? That seems to be a fascinating subject to investigate. Second, Carroll thinks his team has a better chance of winning close games if he can manage the game to put his coaching staff and Wilson in that position. This belief runs counter to the conventional wisdom in the analytics community that winning close games is random.In general, that conventional wisdom makes sense: close games tend to be decided on a small number of plays (or decisions including by the refs) that would seem to even out over time. However, there is some interesting work being done in basketball suggesting winning close games can be a skill. Certainly, the eye-test watching Wilson, Aaron Rodgers, Tom Brady, Patrick Mahomes, et al, supports the notion that "you don't want those QBs to have the ball last." Do these QBs behave differently in crunch time? For Wilson, that becomes the time he is allowed to "cook."What is fascinating about Seattle is that they began a rebuild for the '18-19 season after missing the playoffs. Rather than eating a couple of losing seasons, they made the playoffs. One would think that Carroll would get credit for "reloading" on the fly. Instead, implicit in the "Let Russ Cook" argument is that this team was closer to winning a Super Bowl these last two seasons than suffering 6-10 records. In college basketball, Carroll's tactics would elevate him to the genius level in the conventional wisdom of that sport.Now in Year Three of the rebuild, I was of the belief that Seattle would pass more this season — just as they did when they were making Super Bowl runs with Wilson (with a better defense and overall roster). I await the 538 dot com article where credit is taken for Seattle’s shift in tactics.I'm not a Carroll stan. I have issues with his approach to the offensive line. Rather, I am a stan for answering arguments -- both explicit and implicit. Fortunately, the misguided conventional wisdom on the Seahawks has contributed to point spread value in the last two seasons. So that has been good!As we now approach the halfway point of the NFL season, what if one of the unintended consequences of "Let Russ Cook" was a decline in play by the Seattle Seahawks defense? More early-down passing shortens Seattle's time of possession on drives. Consider these defensive numbers:2020 Seattle Defense (after Week Six): 27.0 PPG, 471.2 total YPG. 6.4 Yards-Per-Play allowed. 2020 Offense Average Time of Possession: 28:21. 2019 Seattle Defense: 24.9 PPG, 381.7 total YPG. 6.2 Yards-Per-Play allowed 2019 Offense Average Time of Possession: 31:26.Last year, the Seahawks had a 30/32 average run-to-pass play ratio during the regular season for a 51.6% pass rate per offensive snap. Now in the Let Russ Cook era, Seattle enters Week Eight of the NFL season with an average run-to-pass play ration of 25/36 fora 59.0% pass rate per snap offensive snap.Some defensive coaches claim that their players only have about 50 plays in them per game. When defensive players go beyond that point, then their energy level begins to decline. Of course, these defensive coaches can not code R to save their lives. Was "establishing the run" Carroll's method to elevate a mediocre defense?  A closing thought from two-time Super Bowl champ (but failed R coder) Jimmie Johnson: "How you protect a defense is you eliminate the negative plays, and you increase your time of possession by running the football." Best of luck for us — Frank.

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Soccer Handicapping Analytics: Expected Goals (xG)

Tuesday, Jul 14, 2020

Analytics can be a powerful tool in the handicapper toolbox when assessing potential value versus bookmaker odds. While statistical analysis has existed with sports since someone started keeping score, the analytics movement examines data to foster a better understanding of the sports we study and follow rather than relying solely on traditional statistics. Often this data can be more predictive of future activity and results than the conventional statistics. This offers exciting possibilities for sports bettors with the opportunity to deploy more accurate predictive data that a majority of the bettors in the market are not using. In baseball, the two most prevalent statistics associated with starting pitchers are Win-Loss record and Earned Runs Average. A statistical analysis of Win-Loss record determined that those numbers had little predictive value for that starting pitcher’s future performance. Data analysis went even deeper to discover that Fielding Independent Performance data such as strikeouts, walks, and batted ball activity offer a more accurate perspective of how a starting pitcher will perform in the future. Statistics such as FIP, xFIP, and SIERA are attempts to provide more accurate descriptive and predictive measurements of how many runs a pitcher allows. In soccer, the idea of expected goals serves a similar vision. Goals scored and goals allowed may be definitive in determining a final score but that does not mean that those numbers are the most predictive regarding future scores. What are other statistics that are important in scoring goals? Most goals are scored by shooting at the net in open play (with exceptions being penalty kicks and opponents scoring own goals which do not appear to be reliable events that can be created without luck and the random behavior of an opponent). The more shots a team takes at the net, the more likely they will score. And the better quality of these shots, the more likely they will get past a keeper. Expected goals is a metric that determines a statistical probability on every scoring chance a team generates in a match. In this adventure of quantitative analysis, similar scoring situations are logged to determine a scoring probability from a deep data set in a way similar to measurements that predict the accuracy of an NBA shooter attempting a 22-foot corner 3-pointer. Shot attempts that have an empirical success rate of 35% or higher have been categorized as Big Chances. By reassessing a soccer match from the expected goals (xG) and expected goals allowed (xGA) given the activity and nature of all the shot attempts in a match. If xG analysis offers a better evaluation regarding how a team is playing, then it could provide a more precise way to measure subsequent action. For example, Southampton entered match week 32 of the 2019-20 English Premier League season with 38 goals scored. However, their xG of 44.20 suggested that they should have scored at least six more goals on the season given the average likelihood of events regarding their scoring opportunities. Bettors that decided that this information was evidence of the Saints covering the goal-line spread with their match at Watford or that the final score would finish over the 2.5 total were rewarded with Southampton’s 3-1 victory. Armed with expected goals and expected goals allowed data for both teams in an upcoming match can offer handicappers a powerful weapon in exposing the hidden value against the posted side and totals numbers of the bookmaker. But these potential strengths of using expected goals data do come with some caveats. There are some disadvantages to relying on expected goals data exclusively. For starters, one should not consider this objective data. At the beginning of the statistical endeavor, there is a human being assessing and categorizing shot attempts (even if eventually this analysis is then replicated by artificial intelligence). The mathematical formulas are all creations by human beings that are deployed in the quantitative analysis. As long as we live in a pre-Singularity world, this phenomenon is inevitable. And it is ok! Just remember that with the human eye and the touch comes the possibility of human error. There are competing expected goals systems in the marketplace. While ERA and field goal percentage are agreed upon statistics, xG remains a proprietary activity with different agents developing and propagating their numbers. Second, the concept of overachieving or underachieving can be misused. Expected goals attempt to determine the most likely outcomes. But not all outcomes are created equal. Lionel Messi is going to score more goals than Glenn Murray dribbling up the left-wing and talking a shot from 30 yards out. Ederson is more likely to make a spectacular save in that situation than Tom Heaton. While xG attempts to minimize outlier efforts, some players have earned their outlier status on both ends of the equation. Betting against Real Madrid (or taking more Unders) because their number of goals scored seems to be overachieving their expected goals may be foolhardy because they have Lionel Freaking Messi! Similarly, banking on bad teams to start playing closer to their expected points calculation (xPTS — a formula attempting to incorporate xG and xGA to reproduce their expected points for the season) may be foolhardy because that team may truly embody the outlier bad xG and xGA numbers. Third, be careful to not confuse recent results as overachievement (or underachievement) when what may be going on is the in-season improvement (or decline) of a team’s quality of play. Teams do get better (or worse) as the season moves forward. Coaching matters. Players improve. Injuries sometimes have disproportionate impacts. Teams can suffer from a loss of morale. An assumption in analytics that attempts to describe past results for predictive value moving forward is that those past results remain a credible assessment of the team’s quality. Yet team quality can be fluid. Fourth, regression to the mean is a long-term expectation so finding discrepancies between current results with expected goals results may not immediately produce dividends. Be patient. And remember what John Maynard Keynes said about the long-run (to paraphrase, we are all dead). Waiting for what may seem to be inevitable regression can be Fool’s Gold. Last, keep in mind that because the margins are thinner in soccer, the impact of expected goals is smaller. In basketball, identifying discrepancies between an expected score and a projected score using Points-Per-Possession analytics can be more fruitful since a college basketball game averages around 130 combined points per game with an NBA averaging over 200 combined points per game. Because soccer generally sees one zero to six combined goals scored per match, there are fewer scoring opportunities for which the discrepancies exposed via expected goals analysis translates into an actual difference in score. Your team can dominate their opponent on the pitch but still settle for a 1-1 draw. Because there are more scoring opportunities in basketball, the expected value identified from Points-Per-Possession analysis has more opportunities to demonstrate itself. These caveats aside, expected goals is a valuable tool to help the handicapping of soccer. Despite Liverpool winning the 2019-20 English Premier League championship, the xPTS analysis still projects Manchester City to be the better team this season. Those of us that used that information to help to conclude side with Man City in their July 2nd meeting were rewarded with a 4-0 victory. Relying on expected goals analysis alone will probably not be profitable. However, adding expected goals into the array of angles from which to determine value relative to the numbers that the bookmakers have posted should make successful soccer handicapping even more lucrative. Best of luck for us — Frank.

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