In Scorecasting , University of Chicago behavioral economist Tobias Moskowitz teams up with veteran Sports Illustrated writer L. Jon Wertheim to overturn some of the most cherished truisms of sports, and reveal the hidden forces that shape how basketball, baseball, football, and hockey games are played, won and lost.
Drawing from Moskowitz's original research, as well as studies from fellow economists such as bestselling author Richard Thaler, the authors look the influence home-field advantage has on the outcomes of games in all sports and why it exists; the surprising truth about the universally accepted axiom that defense wins championships; the subtle biases that umpires exhibit in calling balls and strikes in key situations; the unintended consequences of referees' tendencies in every sport to "swallow the whistle," and more.
Among the insights that Scorecasting
In an engaging narrative that takes us from the putting greens of Augusta to the grid iron of a small parochial high school in Arkansas, Scorecasting will forever change how you view the game, whatever your favorite sport might be.
Finally, a book for those of us who read Moneyball and thought, "but I was told there would be math." This is a comprehensive statistical analysis of the unifying themes in all sports. Want to know why teams have a home field advantage? Just want to see mathematical proof that there is a home field advantage across all sports? Curious about whether or not "defense wins championships"? The answers that you seek are here.
More importantly, the fact that the authors look at a number of different sports in this analytical way keeps the book fresh and interesting. When the reader has heard enough about Tiger Woods or Michael Jordan, the focus switches to hockey, tennis, or baseball. It is very well done.
I highly recommend this book to anyone who likes sports or math.
This is basically Freakonomics for sports. And I say that as a good thing. A scholarly read that is still fun. The authors are intellectual but can still be regular fans. Not always easy to read as there are lots of statistics and numbers but if you are patient and think your way through they make good arguments for such subjects as Does Defense Really Win Championships? and When Do Refs Choke The Most?
There are two chapters in this book that should be read by anyone hoping to better understand sports outcomes, spanning pages 110-167 and breaking new ground concerning why teams win a higher percentage of home games than road games.
The conclusion is this - referee bias from social influence is the leading cause of home field advantage across all team sports
Baseball – Close pitches go the home team’s way more often, and most commonly in high leverage situations. The larger the crowd and the closer the crowd is to the refs the more pronounced the effect.
Soccer – Stoppage time in close games goes the home team’s way to preserve the lead/give them more of a chance to tie/win; no effect in blowouts. Yellow and red cards go the visiting teams' way at much higher rates, obviously compromising their ability to win with one fewer player or with player(s) reducing their aggressiveness to lower the risk of being ejected on a hard tackle, etc.
Basketball – Loose ball fouls, balls tipped out of bounds, charges vs. blocking fouls go the home team’s way; some home field advantage is also explained by teams legitimately playing worse on 2nd night of back to backs and road teams playing more of those than home teams
Football – fumbles, fumble recoveries, penalties that result in first downs - all go the home team’s way more often, once again more commonly in high leverage situations at the end of games
College – same as above, but big schools scheduling patsies makes the college advantage even greater than the professional home field advantage
Highest ref influence in soccer; lowest in baseball. No difference between ref bias in same sport no matter the country or the league. Players show no difference in ability home vs. road, crowd vs. no crowd
Chapter on the NFL draft is mostly a disaster. Mike McCoy of the Cowboys used trade history to create a chart showing how much teams value draft picks. The Cowboys used this very effectively, obtaining higher value picks and players from less sophisticated teams, helping begin their run of 3 Super Bowls in 5 years in the 90s. Other teams used the chart, advantage lost, chart re-evaluated based on performance. Giant, painful, statistically hollow evaluation, based on research by one of my former business school professors, follows. Player value was assigned based on starts. (Yes, I’m not making this up.) Starts show durability, but that’s about it. Painfully inept here, and some of the credibility the authors had established in the HFA chapters was lost on what looks like a deliberate misrepresentation of value in order to prove a point. Throw in that they tied the salary per average draft pick using pre-2010 data, i.e. before the money was slotted rather than free market insanity, and you’ve got an intellectually dishonest one-star chapter. For shame. I kept the overall book rating at 4 stars for the outstanding work on home field advantage, but certainly considered dropping it to 3 stars for this bad chapter alone.
Moving on, the baseball players most likely to take PEDS are young guys from poor countries and American players in the 35 and up crowd. Both are responding to economic incentives. The impoverished kid from the Dominican Republic is trying his best to get a contract to raise the living standards for him and his family while the older player is trying to get one last contract on dwindling skills.
Icing a kicker doesn’t work, nor does icing a free throw shooter
The “hot hand” is a myth, not borne out by statistical observation
The Cubs aren’t cursed. They just consistently send out a below average product. Why? Their fans show the least sensitivity in terms of willingness to pay vs. winning percentage of any MLB team. What incentive do the Cubs have to put tons of money into their players, scouting, etc. if people will buy tickets no matter the team’s record? This seems very similar to the LA Clippers’ NBA strategy from the 70s until 2009. People will pay regardless, so why spend more money to win?
In high leverage situations in baseball an umpire is more likely to call a borderline pitch in a way to prolong an at bat, and in basketball as well as football a referee is less likely to call a foul/penalty in such situations. Why? They are attempting to remove themselves from the outcome of the at bat or play. NBA refs are also less likely to put a star player in foul trouble and are more likely to give them the call on loose ball fouls. Also, the evidence shows makeup calls do actually happen quite frequently, as officials attempt to balance out a blown call in order to remove themselves from deciding the outcome of the game. All of this is of course ironic considering referee bias brought about by reacting to the collective influence from the home crowd is the primary source of home field advantage in team sports.
Loss aversion, which I think I’ve read about now in 5 books as well as covered in 2 business school classes, is part of what drives coaches to make the wrong decision on going for it on 4th and short yardage in football. Statistically, a team would improve its chances of winning by going for it more often in these situations, especially on the opponents’ side of the field, but fear of a turnover on downs prompts punting more often than it should.
This same force, loss aversion, is also the reason golfers are more likely to save par than to get a birdie on putts at the same distance. Humans hate loss more than they love gain. This is a flaw in the brain’s reward assessment ability and your ability to recognize and exploit it can improve outcomes for you in business deals and negotiations.
Not covered in the book, but something I observed myself in terms of loss aversion is that a basketball player who has just turned the ball over is much more likely to commit a risky foul in an attempt to get the ball back in the few seconds after that turnover than he is at any other point of the game up to the final few plays. Why? He’s already in negative mental territory, having given up possession, so he’s now willing to take on risk in order to get back to neutral ground, as the further “loss” of picking up a foul will have a much lesser mental impact than did the turnover, or than would a steal.
The endowment effect makes you value something of your own above its actual worth.
Offense wins championships at the same clip as defense, but since defense carries less glamour it’s a good idea to preach the virtues of defense in order to get players to give as much effort on that side of the ball.
Blocking a shot out of bounds is not very valuable. Blocking a shot to a teammate or at least making it a loose ball by keeping it in bounds is very valuable.
Closing a baseball game by coming on with no one on base and then getting three outs with a 2-3 run lead to get a save is of negligible value. Coming into a baseball game with runners on base and retiring the side without giving up a run is of very high value. Failure to recognize this is part of what drives the poor managerial decisions to send a team’s best reliever out to pitch in situations dependent upon what inning it is rather than dependent upon how high a leverage point it is.
Very few MLB hitters bat .299 for the season because a player coming to the plate in the season’s last game with a .299 average is very unlikely to take a walk while a .300 hitter will likely be pulled from the game. Big round numbers - .300 average, 30 home runs, etc. – lead to higher contracts so players are incentivized to achieve them even if batting .300 vs. 299 is in effect meaningless. The wise General Manager would sign players who hit in the high .290s for less money in what is essentially an arbitrage situation when comparing those players to batters who hit .300.
scorecasting is marketed as the freakonomics for sports… i feel like if i outright admit enjoying something like this, it also means admitting that there’s a leviathan chance of having a lot in common with two of the most loathsome species on earth: finance and sportsbros. and i’d rather torture myself by watching an adam mckay movie than admit that. ( i work in finance to pay my bills and i watch sports the way a cishet male watches beauty pageants, okay? i would never let anyone crucify me for my source of living and my objectification of overpaid himbos) but let me just ingeminate some of the authors’ conclusions I found slightly interesting:
● omission bias. suggests that the rate of officials’ calls will decrease as the score gets closer. fans and athletes expecting officials to remove themselves during the key moments of the match. (came with great illustrative comparison between mike carey in super bowl xlii and tsurubichi in the 2009 us open) ● the reality that refs are uncannily accurate 90% of the time and that maybe I should stop yelling at my tv screen every moment i think i know better than someone who has been doing his job for yearsss ● although makeup calls really are a thing ● coaches receive more blame (and has a wonderful opportunity of contributing to the unemployment rate) when their teams lose by implementing new tactics; compared to them losing by sticking to their formulaic predictable plays. see: hindsight bias ● also see: loss aversion ● michael jordan is one clever liar of a man because defense doesn’t actually win you championships ● defense does gain more flack if the team gives up a score but gets little praise if it does a good job—no matter how vital it might be to the narrative of the game… there’s a reason why fans exhort “de-fense! de-fense!” and not “of-fense, of-fense!” ● earl woods walked so yolanda hadid and kris jenner could run ● even confronted with statistics, people would still look at you like you’ve sprouted two heads. ● like wdym momentum in sports doesn’t exist?? ● lastly, i now begrudgingly know what the fuck is a slump-buster. And no, this isn’t an interesting point, i just never miss a chance to emphasize how putrid men are.
Yeah, it’s math. And yeah, lots of fans still won’t trust the data. But the stories are great and gives fans fun stuff to reminisce on. I wish there were insights for fantasy leaguers, and a lot more football, but it’s a great addition to the Moneyball bookshelf.
I love this book in theory, but, much like Freakonomics before it, the reality is disappointing. The pedestrian writing repeats itself innumerable times - to the point where I would almost recommend reading only every other paragraph (and maybe skipping the first and last sentences of those paragraphs). Several of the topics covered will be very familiar to anyone who follows intelligent sports reporting - the hot hand isn't real, calling a timeout to "freeze" your opponent is ineffective, coaches don't go for it on fourth down enough - and many of the psychological studies reported herein have shown up in the books by similar thinking authors - Gladwell, Lehrer, Levitt - many times. On the plus side, a large portion of the book is dedicated to an in depth look at the reasons for home field advantage across a variety of sports and some of the other topics are quite interesting as well. The research seems thorough and the conclusions sound. If you have some interest in taking the myths out of sports and have not read anything like this anywhere before, then I would recommend this book to you. Otherwise, you can just skim the parts that are less familiar to you.
It's Freakonomics applied to sports. Unfortunately, this book doesn't hold attention quite as well as Freakonomics, but if you're a sports fan and a math nerd it's an interesting read. I think the book would have been better as a series of articles. Each chapter seems to stretch out as many pages as it can out of some pretty dull analysis. The book is at its best when it makes it point, provides a little bit of data and moves on. Toward the middle of the book the authors lag on some longer themes, but overall it was a quick enjoyable read.
Ironically I happened upon this book by chance. I'm glad I did. I think anyone with a love for sports and a basic understanding of statistics will enjoy this book. I was impressed with the authors' abilities to provide great statistical and logical analyses without negating the human element and without taking sports too seriously. It was a fun read and would make a great book club selection.
Full of stats and explaintions. I say someone compare it to Freakanomics or sports. Pretty fair comparison. Numbers can help explain slumps, "luck" (good and bad), and even the use of PEDs. Fascinating stuff, this book.
That was a fun read for me. It mixes sports with economy and psychology and also has lots of numbers. The book explores the common biases and myths in the world of sports. For example, the omission bias in referees, how players and coaches suffer from loss aversion, the myth of the hot hand and how the top drafted players are overvalued, among other things. The most fascinating chapter for me was the one about home field advantage and the massive role played by the referees on that. The book has a also nice bibliography with the academic studies mentioned in the chapters.
The authors take a "Freakanomics"-style approach to sports "truisms", debunking some myths (don't bother icing the kicker) and breaking others down (the real reasons behind home-field advantage). I don't have a good reason to do so, but I'm going pros and cons for this review.
Positives: * Many of the findings were fascinating - the Mitchell Report data, the .299 hitter bit, breaking down the difference in value of blocked shots based on what happens to the ball subsequently, the size of the strike zone based on different situations and prior incorrect calls - all good stuff. * The authors make a strong case throughout that the tried and true statistics in sports can be very misleading. They give example after example where you have to dig deeper to understand the true value behind the numbers * They gather research from a number of different sources to build a good volume of examples, and give credit where credit is due.
Negatives: * The authors do a nice job on much of the statistics. However once they have those they insert their own opinion as to what those statistics mean, and then speculate further assuming that their presented opinion is a proven fact. I forget the name for this (making assumptions based on assumptions), but it really does not help them make their case. * One chapter compares the championship distribution in MLB and the NFL. It takes two small data sets, compares them to each other, and comes to the conclusion that the NFL is more competitive than MLB (which is essentially the public perception of the two sports). In many other places in the book the authors go into great detail about controlling for different factors, sample size, etc... In this chapter they don't mention any of them, leading me to assume (based on how thorough they are in other places) that they didn't look at them. * Frankly, the writing comes off with a tone that says "we are way, way smarter than you" and "you can't question any of this because the data says we are right". Those things might both be true (never met them, don't have any reason to doubt their data), but it is fairly annoying to read.
Bottom line - I really wanted to like this book more than I did. However, the highlights were not strong enough to overcome the challenges - therefore it was just OK for me.
I’m a sports enthusiast and a researcher interested in organizational behavior and information systems. This book was right up my alley!
It would be interesting for you to follow up to the chapter on whistle swallowing since you can now evaluate the accuracy of every single pitch in baseball. That provides a larger data set to particularly look at those calls that are or are not made. It would also be interesting to look at the speed of the pitch in baseball vs. success (of pitchers and hitters). With so many pitchers hitting triple digits now, does that help them achieve success? Or is it more effective to follow a Greg Maddux model and choose accuracy, movement, and pitch location over speed? As more athletes are undergoing Tommy John or other surgeries at younger ages – do they improve their chance of making it to MLB or even being draft, when they pitch faster? Or does pitching too fast and too early make it less likely that they will ever make MLB because they become injured? Similar to the Dominican player example you used later in the book, is it worth increasing the chances you will have arm surgery, simply to get more speed on your pitch at an earlier age? Little League Baseball and other youth baseball organizations would likely be interested in your results. An organization like Perfect Game, for instance, charts the speed of every single pitch, beginning at 13 years old or younger. How does the addition of a radar gun change the way that kids practice and pitch in a game? Is it damaging their arms? I don’t have the answer, but it would be an interesting study. Or, like the Dominican player, is it worth it to pitch faster at a younger age, in spite of the dangers of having to undergo surgery? In other words, is the return (likelihood of getting to MLB) worth the risk (of season- or career-ending surgery, perhaps early in your career, when you are still a teenager)?
I would love to see more football teams go for it on 4th down! I am a long-suffering Georgia Tech football fan. We run the option now, and our coach tends to go for it on 4th-down-and-short more often than other coaches. He thinks he can get a couple of yards every play – not a lot of yards, but a couple. The likelihood of kicking a great punt isn’t that high for Georgia Tech, either. So they go for it – not as often as the high school team the authors studied, but more often than others.
You definitely argued for the value of a 0.299 hitter as opposed to one hitting 0.300. That’s along the same lines as Moneyball argued, looking at the value of a player versus the cost of that player. Everyone doesn’t have the unlimited money that the Yankees seem to be able to find. The same should hold true for a pitcher, I would assume – but I don’t know what the data would support. Does a pitcher with an ERA of 1.99 demand significantly more salary than one with an ERA of 2.00? Or 2.99 compared to 3.00? I would be interested to see what the data suggest for those questions.
The chapter on African-American coaches in the NFL was short. I’d like to see more updated information, with a larger sample size. Do you find the same results with other underrepresented groups in coaching roles? How about the NBA? How about female head coaches for women’s college sports teams? Why do we have men as head coaches for some women’s college sports teams – but no women head coaches for men’s teams? I’d ask the same question for officials. Why do we continue to have large numbers of underrepresented groups (generally African-Americans, women, Latinos, etc.) as coaches and officials across high-school, college, and professional sports teams?
You make lots of assumptions in the chapters on home-field advantage. In baseball, for instance, do better pitchers tend to get borderline calls, at home or on the road? How does the skill level of the pitcher affect the balls-strikes called? For instance, does Kershaw get more home and away calls, no matter the batter, the situation, or the size of the crowd? Similarly, does Altuve get more balls-strikes called in his favor, no matter where he is? Baseball is often described as a pitcher vs. batter situation. There were too many assumptions in this chapter for me to fully buy into the reason for the home-field advantage. Do some teams get the home-field advantage, even when they are on the road? If Duke men’s basketball plays Eastern Illinois on the road, will Eastern Illinois get the close calls at the end of the game, or does Duke get calls wherever it plays? You may have more data but kept it simple for a trade book, but I’d like to have more details and less assumptions in this section. Further, you suggest causality when you only have correlation between variables (as # of fans increase, the number of calls going the home team’s way also increases). Assuming these are statistically significant correlations, that’s all you can say. One doesn’t cause the other, but the two do move together in predictable ways. I’d suggest testing causality if you haven’t done so already. What if the stands are filled with significantly more visiting team fans than home team fans? Do the officials then favor the visiting team? Has the option of video replay in MLB gotten rid of the home field advantage? That would be interesting to see.
As I started to question the assumptions in this chapter, I became more critical of the rest of the book and less satisfied with what you reported. Much like someone with the “hot hand”, once I start to question the interpretation of the data, I questioned the rest of the book – and even earlier parts of the book as well. If you made assumptions in this chapter, perhaps I need to read more closely in the rest of the book.
Does your theory about draft order, the value of current/future draft choices, etc. hold true for other professional teams outside the NFL?
I enjoyed reading your speculation about the Mitchell Report and why certain players – minor vs. major league, those from Latin countries, and younger/older players – are more likely to use PEDs than others. From a socioeconomic viewpoint, I agree with your conclusion. If you’re from the DR and trying to get to the majors, why wouldn’t you use performance-enhancing drugs? The risk is worth the potential reward.
Your chapter on “Damned Statistics” hit the mark. I get so tired of seeing a statistic on screen that says something like, left-handed batters on the road playing at night in domed stadiums have hit 0.400 on their birthdays, or something equally as inane. Someone is sitting at a database doing random searches and coming up with such a contrived situation, and it’s often meaningless.
The chapter on the Cubs appears to be measuring correlation and not causality. You likely want to re-do the chapter, anyway, since the long-suffering Cubs finally won a World Series!
Overall, I enjoyed the book, and it was an easy read. I question some of the assumptions that are made without supporting data, and the authors often suggest causality when they are reporting correlation. But still, the book is a good read for anyone interested in sports and statistics – or anyone with an opinion or icing the kicker, betting on the team that is on a winning streak, or trying to understand why people behave in certain ways, given the risk-reward opportunities.
Enjoyed the read, albeit several themes were unnecessarily dragged out after the point was made. It is an intriguing book for sure.
Some excerpts for my own future reference: ___________________________
GO FOR IT This same loss aversion affects coaches. They behave much like the shortsighted mutual fund manager who forgoes long-term gains to avoid short-term losses. The coaches are motivated less by potential gain (touchdown) than by fear of a concrete loss (the relative certainty of points from a field goal). ___________________________
BELICHICK Is it a coincidence that Patriots coach Bill Belichick opts to go for it on fourth down more often than any of his colleagues? Before he became a star coach he approached the game quite differently. Belichick amassed an unimpressive 45% winning percentage and had only 1 winning season in 5. When he was with the Browns he went for it on 4th down 1 of 7 times. Since taking the helm of the Patriots in 2000, Belichick has gone for it on 4th more than 1 in 4 times. One could argue that having a better team he was more likely to convert on 4th down. True, but the differences were significant (59% vs 51%). So what changed? Belichick didn’t have job security in Cleveland. Only after his Patriots won multiple Super Bowls his 4th down risk taking increase significantly. ___________________________
COMPETITIVENESS As a rule, we’re offended by oligopolies and monopolies. We much prefer competition; it's healthier, its better for consumers, it encourages innovation, it just feels fundamentally fairer. We have antitrust laws to promote competition. In the heavily regulated airline industry, the largest carriers in the domestic market, American and Southwest, each have less than 14% of the market share. Banks, too, are heavily regulated, so much so that under the so-called Volcker Rule, no institution may exceed a 10% market share. Citigroup may have been deemed “too big to fail”, but its market share was only 3%.
The Yankees? Inasmuch as World Series rings constitute a market, their market share is 25%. How can one team dominate while other teams are barely competitive? Money. Yankees payroll in 2009 exceeded 200 million, whereas the Pittsburgh Pirates was 39 million. ___________________________
LOSS AVERSION Loss aversion influences everything from everyday decisions to athletic performance to individual investments. Thanks to loss aversion, we tend to place a higher value on objects we own than on objects we don’t even if it’s the same object. In theory, our willingness to pay for something should be the same as our willingness to be deprived of it. This phenomenon, related to loss aversion, has a name-The Endowment Effect-coined by Richard Thaler, a behavioral economist. Thaler found that people feel the loss of something they own much more deeply than they feel the loss of something they don’t own.
Two behavioral psychologists at Duke University performed an experiment using basketball tickets. Duke has a successful basketball team and extremely small arena, the 9,314 seat Cameron Indoor Stadium. Demand greatly outstrips supply. For important games they raffle tickets to avoid students camping out in front of Cameron for weeks at a time. The psychologists posing as ticket scalpers, asked those who had not won a ticket in the raffle to tell them the highest amount they would pay for a ticket. The answer was $170. Then they asked the students who had won a ticket for the lowest amount at which they would sell, the answer on average was $2,400. ___________________________
ROUNDING FIRST Why are .299 hitters are so much more rare than .300 hitters? Whether we’re buying batteries at Walmart, a fast food value meal, or even a house, odds are good that the price ends in a nine. So given the stakes, hitting .300 is a goal of paramount importance among baseball players. Researchers looked at hitters batting .299 on the final day of each season from 1975 to 2009. One hit and player could vault above the .300 mark. With a walk, they wouldn’t be credited with an at-bat, so their averages would remain the same. Not surprisingly, the research showed that players hitting .300 walked 15% of their at-bats, and players batting .299 walked 5% of the time, but in their final at-bat of the season NO player hitting .299 has EVER walked.
THERE’S NO “I” IN TEAM What about the notion that a lineup of five solid players is better than a starting five of one superstar and four serviceable supporting role players? One way to test this idea is to look at the disparity among a team’s starters in terms of talent, or a reasonable metric of salary. Controlling for the average salary and winning percentage of teams, do teams with bigger differences in salaries among their starting players fare worse than teams whose salaries are spread more evenly among the players? We find the exact opposite, in fact. Teams with the most variable talent across their players are more likely to make the finals and likely to win a championship.
At his 2009 induction into the Basketball Hall of Fame, Jordan gave a speech that revealed much about his competitive drive. He told a story of once scoring 20 consecutive points late in the game to lead to victory. Afterward, he was admonished by the Bulls longtime assistant coach Tex saying, “Michael, there’s no “I” in team.” Jordan recalled his response… “I looked back at Tex, and said, ‘There’s an I in win. So which way do you want it?’” ___________________________
WHAT ISN’T IN THE MITCHELL REPORT It turns out that although American players account for 74% of those in the MLB, they represent just 40% of suspended drug offenders. In contrast, Dominicans represent 10% of MLB players but account for 28% of drug offenders. Venezuelan players account for 6% of MLB players but 12% of those suspended for drugs.
Why this huge disparity? There are a host of plausible explanations for this complex issue, touching on everything from culture to education. But ultimately, we’re convinced that simple economics does the best job of explaining why players from well-off countries are 5 times less likely to test positive for PEDs than players from impoverished countries. When players cheat or circumvent rules, they consider a trade-off between risk and reward, balancing the potential advantage of the gain against the possibility and cost of getting caught and the punishment they would face.
Under baseball’s old testing program, the incentives to cheat were high for all players, but they were particularly extreme for Dominicans and Venezuelans. Never mind the lure of a guaranteed MLB contract, which could ensure generational wealth. Consider Welington Dotel and his $160,000 MLB signing bonus. That is as much as his parents might make in decades of working.
The stigma of using drugs may differ across countries, and there may be a self-perpetuating effect. The greater proportion of Latin players who use such drugs, the less stigma it carries. The more players who use banned substances, the more pressure it puts on others to cheat. If your class is being graded on a curve and you know many peers plan to cheat on the upcoming test, you’re probably more tempted to cheat too.
Questions about sports that always come up: Why is the home team so often successful? Is there no I in team? Why are there more .300 hitters than .299 hitters? Why do golfers suck it up to avoid bogies but play it safe when looking for a birdie? In football, why is it so normal to punt on 4th down, no matter the situation? The4se and other issues are the focus of this quirky--but fun--volume. Sports fans will enjoy this; so, too, will students of the human condition.
I don't want to give away too much here, but let's provide one simple example. Why home teams do so well in many sports. The authors examine several standard explanations: Home crowd support drives home field success; Travel saps visiting teams and, thus, they tend not to do so well on the road; home fields have some unique characteristics that the home team takes advantage of (e.g., stadiums in cold weather cities in pro football). However, the data do not support the series of suppositions raised by the authors. What seems to make a difference? The officials! They tend to cut home teams slack.
Psychology provides an explanation for some of the phenomena observed. For instance, humans are risk averse. They overvalue negative events, so over respond to them. The threat of a bogey is more motivating than the desire for a birdie. So golfers "go for it" when faced with a bogie and "play it safe" when tempted to gain a birdie. Data suggest that Tiger Woods as well as many other golfers fit this pattern. Risk averse behavior is, the authors contend, the explanation.
And on the book goes. Again, for the sports fan or those interested in the quirks of human psychology, this will be an enjoyable and enlightening work. After all, many of the lessons here can be generalized to other parts of life rather than just sports. . . .
This is a very interesting book, highlighting some interesting findings I was familiar with--such as that NFL teams "go for it" on fourth down way too infrequently, that there is little evidence for systematically "hot" players, and that draft picks later in the draft are undervalued relative to top picks--but also introduces several interesting new pieces of research.
The authors do a lot of work on home-field advantage, finding that players do not actually perform better or worse at home versus on the road (looking at things like shooting percentage, how many swings make contact, etc.), but that the referees favor home teams in judgment calls. The data showing how large the strike zone is for home versus visiting teams is particularly informative. (Interestingly, non-judgment calls, like whether a player steps out of bounds, do not favor home teams.)
Another interesting piece of analysis is that NBA teams perform significantly better at home in part because the schedule is such that the home team is less likely than the visiting team to be playing for the second night in a row--something I hadn't realized but that makes a lot of sense.
The authors also dispel the myth that the Cubs have been unlucky--really, they have just been bad. And their attendance, contra other baseball teams, is relatively inelastic to the team's performance, so ownership/management has much less incentive to build a winner. One related piece of data that made me laugh--Cubs attendance is relatively elastic with respect to beer prices. And the team has among the highest ticket prices in the league, but the third-lowest beer prices. Ha!
A great book, highly recommended to anyone interested in an analytical approach to sports. Better written, and more new material, than the books by David Berri.
Freakonomics with sports, or as people on Japers' Rink call it "FANCY STATS". Like Freakonomics, Scorecasting likes to turn people's perceptions on their heads, like determining why sports have home field advantage (spoiler alert: it's the refs).
The problem with a book like Scorecasting if you're a mathematics or economist or anyone who knows something about statistics is that you want to ask questions of the analysis. Things like sample size, how the authors controlled for various factors, correlation vs. causation. In a book that likes to state definitive "facts", I kept wanting to ask questions of the authors. Instead of being a final answer, the conclusions in Scorecasting felt a lot like a good start.
There were also a couple chapters in the book that were exceedingly short, like the chapter on black coaches. That particular one rankled me because black coaches in the NFL before the Rooney Rule were such a small sample size that trying to draw meaningful analysis out of the data was foolhardy. Those chapters were probably best omitted or turned into a column with interviews and descriptions. The stats themselves could not stand on their own.
So, I enjoyed the book and it was a decent addition to the rest of the behavioral economics and evolutionary psychology books I enjoy, but with the breadth of the book (ALL THE SPORTS) and the brevity of the book (it felt much shorter than Freakonomics especially with the very small, light chapters), the book felt incomplete and shallow. The authors kept making broad pronouncements and I kept wanting to delve deeper into each subject but instead was bounced around from subject to subject. I would have preferred one deeper examination of one sport or one aspect, rather than the wide net that authors cast but failed to reel in.
"Scorecasting" is one of those books that not only don't reward close reading, but actually wind up discouraging it -- and not because the basic ideas are lacking, but rather that those basic ideas are pretty much all there is.
It's like "The Seven Habits of Highly Effective People": All you need to do is read the habit itself, and you've pretty much covered the topic. The rest of the chapter is essentially filler, and that's the story with "Scorecasting" as well. For example, there are two chapters, totaling 58 pages, on why there is a home-field advantage. The answer (that officials want to please crowds, essentially) can be explained in about three. The six pages on blocked shots could be boiled down to a few paragraphs -- and so on.
That said, the ideas, though hardly revolutionary, are interesting, and sports fans will come away having learned something about what influences the outcomes of games. But 255 pages, especially 255 pages filled with examples that are now 10 to 15 years out of date? Seventy-five or a hundred pages, with no examples, would have done the job just as well, so if you pick up "Scorecasting," once you grasp the point, move on.
After all, I think one of the habits of effective people is not wasting time.
Freakonomics for the sports buff... super fast read. Many interesting tidbits here, my favorite the fact teams are better off "going for it" on 4th down versus punting, which is something I’ve always thought. Given this “go for it” attitude is statistically favorable for the average team I have to think it's doubly favorable for the Chargers and their abysmal special teams play. Methinks Norv Turner needs a copy of this book. Also, I liked the numbers and reasons behind “home field advantage” (not what you might assume!) and found it interesting this advantage doesn’t vary wildly amongst various sports. What kept this book from getting more stars is more my fault than the authors’… I just flat out like the NFL and MLB a lot more than NBA and NHL so when the same point was explained through all mediums I got a little bored at times. Also, an entire chapter on “are the Cubs cursed?” seemed unnecessary. Not a sports phenomena I’ve spent a single second pondering. Nonetheless, overall a fun read. Great for the sports fan.
Wonderful book! I thought they made a few errors or were a little too confident in their findings a few times (if you data mine a lot, you're probably going to find coincidences... Even if the statistics appear to say each one is signficant... They also made some displays of data which were a bit misleading - They said the cub's attendance was less win-sensitive than any other team, and they used a bar graph in which the Yankees (I believe it was the Yankees) had a 0.9 sensitivity and the cubs a 0.6 sensitivity, but the graph's bottom was at 0.5 so it looked like the Yankees attendance was four times more sensitive to attendance than the Cub's, even though that was not the case).
I really enjoyed the book and did not feel it was totally ruined by these things.. I would have given it five stars if they didn't focus on looking at the probability of something happening after it happened (a big no-no without acknowledging the limits of this approach), the data misrepresentation, and the data mining without acknowledgement.
CORONAVIRUS UPDATE: early data in the Bundesliga is confirming the author’s views on home bias. Take away the crowds and home advantage disappears. VAR was already seeing a lessening of the impact.
Easy to read and entertaining book applying a combination of behavioural economics and "Freakonomics" style statistical analysis (eliminating other variables and often overturning conventional analysis) to (mainly American) sport. Interesting areas include: home bias being almost entirely due to crowd influence on referees for more subjective decisions (e.g. extra time) and not influence on players (as penalties and free throws are not affected); Chicago Cubs "bad luck" actually being down to economic incentives - the crowd size being almost entirely independent of their success (with interesting impacts - not mentioned - for Newcastle and more recently Arsenal).
I wouldn't say this book was bad, but it was definitely dated. Some of the chapters, most notably the ones about OT coin flips in the NFL, the value of particular NFL draft picks, and whether the Cubs are cursed are now OBE due to either rule changes or on-field events since then. That said, the two chapter portion on home field advantage was excellent, as was the discussion of referees/umpires "swallowing the whistle." Lots of that stuff most sports fan would be able to discuss anecdotally, but the data is extremely interesting as yet another touchpoint.
Not a must-read and some chapters are more enlightening than others given the time elapsed since the book was published, but it was still a fun exercise nonetheless.
I enjoyed this book, and if you're a sports fan and like numbers you should definitely read it. I especially enjoyed the section on home field advantage, as it's something that I've wondered about for a long time. My only complaint is that the whole "why everything you thought was true is wrong" is way overplayed, in my opinion. Clearly, telling us that things we think are true really are wrong is a good way to catch attention and sell books. I just think it was a bit overbearing in this book. That being said, as an economist and numbers guys, I can't complain about the analysis nor the conclusions, and found nearly every chapter interesting.
There is nothing truly new or breakthrough in this book, but it is another solid entry in the sportsonomics category. While the conclusions mostly summarize previous studies, it's hard not to stand on some shoulders in this genre. Anyone who has read Wages of Wins or follows Beyond the Box Score, Fangraphs or Adanced NFL Stats will be familar with most of the concepts and possibly read some of these topics previously. Still, I'd rather someone read this and at least start to look at things differently.
Less Moneyball than a sports-centric Freakonomics. I enjoyed the majority of the topics the authors delve into. I especially liked the review of home field advantage across sports. The research didn't seem as "cherry-picked" as some other recent books I've read - there are many explanations of how they are measuring or evaluating. I didn't appreciate a few chapters where they listed lots of statistics and numbers. On paper you can skip tables, but when they are read on the audiobook version, you are trapped! Overall, though, a fun read.
Denne uka har jeg lest Scorecasting, hvor Sports Illustrateds tennisskribent Jon Wertheim er en av forfatterne. Den kan sammenlignes med og Freakonomics (som jeg ikke har lest), ved at den prøver sportsklisjeer mot tørre fakta. Defence wins championships, som Michael Jordan pleide å si? Lønner det seg å ta en time-out for å sette motstanderen ut av spill?
Boka handler for mye om typiske amerikanske idretter til at jeg klarte å henge helt med. Baseball og amerikansk fotball har jeg ikke noe forhold til.
Scorecasting setter dette i sammenheng med det som kalles omission bias, som de mener er utbredt blant dommere: De aller fleste dommere vil nøle med å blåse hvis mye står på spill. Årsaken er at vi – som tilskuere og som mennesker – lettere tilgir personer som lar være å handle når de skulle ha gjort det, enn mennesker som tar en feilaktig avgjørelse. Derfor lar dommere feil passere. (De har tall som underbygger det også.) Mot slutten av de fleste kamper, uansett idrett, vil dommeren holde seg mer og mer i bakgrunnen når oppgjøret er på sitt mest dramatiske, og la være å blåse i situasjoner som ville blitt avblåst om de hendte tidligere i kampen. (I fotball vil dommeren også legge til mer tid hvis hjemmelaget ligger under, enn hvis de leder, ifølge Scorecasting.) Denne logikken er forståelig, men er den ønskelig? Den åpner for et stor tolkningsrom: Hvilke regler er for ubetydelige til at dommeren skal bry seg om dem når alt skal avgjøres? I tennis: Skal ikke dommerne dømme fotfeil i slutten av et sett? I fotball, skal man la være å blåse av for tvilsomme off sidesituasjoner nå klokka bikker 90 minutter?
Det mest interessante i Scorecasting er logikken som slår inn i golf, og som lett kan overføres til annen sport og livet i sin alminnelighet. En golfrunde handler om å komme seg gjennom løypa (banen?) på færrest mulig slag. Hvert hull på en golfbane har et visst antall slag som er beregnet å bruke, det kalles å klare hullet på par. Scorecasting viser hvordan golfspillere er mer offensive hvis de slår for å berge par, altså unngå å bruke flere slag enn forventet på et hull, enn hvis putten de slår vil sørge for at de klarer det under par. Vi skjerper oss mer hvis vi kjemper for å begrense et tap, enn når vi spiller for å øke en ledelse. Tap svir mer enn seier gleder. Hvor ofte har du ikke hørt noen si «det er ikke så nøye med selvangivelsen, så lenge jeg ikke blir skyldig penger?» Hvor mange som får igjen penger på skatten, sjekker om tallene stemmer, sammenlignet med hvor mange av restskattebetalerne som gjør det?
Scorecasting handler også om hvordan sport har en innebygd konservatisme. Trenere som feiler med en velprøvd strategi, vil sjelden få gjennomgå hvis de taper. Trenere som prøver noe nytt, selv om det er aldri så velbegrunnet, blir kastet til løvene hvis det slår feil. Forfatterne trekker paralleller til aksjemarkedet: Ingen megler vil få sparken for å investere kundens penger i Statoil eller DnB, selv om aksjekursene skulle kollapse måneden etter. Men mislykkes de med investeringer i ukjente selskaper, selv om grunnene skulle solide, kan de begynne å se seg om etter ny jobb. Ikke kom her og tap på nye måter!