Hollywood Sports Sports Picks For Sale

Hot Streaks and Achievements

  • *58%* 261 of 450 All-Sports run since 12/9!
  • *71%* 10-4 NHL Game of the Year/Month run, 4/18
  • *64%* 38 of 59 NBA 25* run through 4/18!

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.

Buy 3 Items, Get 1 Free

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. 

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

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.

Read more

"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.

Read more

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.

Read more

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.

Read more

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.

Read more

All photographic images used for editorial content have been licensed from the Associated Press.

© 2021 Al McMordie's BigAl.com. All Rights Reserved.