Hollywood Sports Sports Picks For Sale

Hot Streaks and Achievements

  • *66%* 97 of 146 All-Sports run since 12/9!
  • *63%* 43 of 68 NBA run through 1/16!
  • *73%* 30 of 41 CBB run through 1/16!

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.

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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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