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Top 10 Moments from the Sloan Sports Analytics Conference 2012

By Dr. Ed Feng Leave a Comment

Bill James on the B.S. Report with Bill Simmons, taped live at the Sloan Sports Analytics Conference

Dorkapalooza. That’s what Bill Simmons called the Sloan Sports Analytics Conference in 2009. Back then, a bunch of mathematically inclined dudes like Dean Oliver and John Hollinger debated advanced stats in a classroom. However, things have changed. Simmons’ article made the conference popular among more main stream sports fans. The 2012 version was Suitapalooza. The average attendee was a few months from earning an MBA, dressed in a suit and spent the conference begging for a job in the sports world. This year, they even stuffed the research paper talks in the far corner of the Hynes convention center in Boston. I didn’t go for the math; I went to talk with people in the halls. Here is my very personal take on the 2012 conference.

10. The gambling panel. When Daryl Morey asked Jeff Ma to participate on this panel again this year, Jeff said he wouldn’t do it unless he could be the moderator and pick the panel. Morey acquiesced, and the panel morphed from stale last year to exciting this year. Bob Stoll of Dr. Bob Sports dished out the football analytics behind his consistently winning picks. Michael Craig of Right Angle Sports was in the audience. The presence of these two companies, the only sports handicappers with profitable picks over the long run, legitimized the entire conference. Add in some tension between other panelists, and it became the hit of the conference. Of course, I wasn’t there because I was at the…

9. The football analytics panel. Disappointing. Can it really be a football analytics panel when no one says the words “expected points”? It didn’t even come up when they discussed Bill Belichick’s famous decision to go for it on 4th and 2 from their own 28 against the Colts. Brian Burke of Advanced NFL Stats used this concept to show that Belichick’s decision was reasonable. Expected points is the cornerstone of football analytics, a concept we explained in this article. No one mentioned it once.

8. Total QBR. I left ESPN’s talk about their Total Quarterback Rating more impressed than when I arrived. Unlike the football analytics panel, they mentioned and even used the concept of expected points. Moreover, the team with a higher Total QBR wins the game 86.8% of the time, a higher percentage than with the traditional quarterback rating. However, they also count clutch plays more, even though no one has ever found statistical evidence of clutch hitting in baseball, a sport with a much larger sample of events. Talking heads like Trent Dilfer insisted on the inclusion of this clutch factor.

7. The line for Bill Simmons. There was no line for the food at lunch. There wasn’t even a line for drinks at the cocktail reception, most likely because each attendee was limited to single free drink. However, there was a long line to get Simmons to sign the copy of Grantland that came in the goodies bag. Just further evidence how his column in 2009 has grown this conference to over 2200 attendees this year.

6. Moneyball. The stage behind many of the panels showed the original cover of Moneyball, the classic book in which Michael Lewis brought Bill James and baseball analytics mainstream. Bill Simmons may have popularized this conference, but Michael Lewis brought numbers in sports from weird hobby to real job possibility for the hundreds of MBA students. Without Moneyball, Sloan sports analytics might refer to an elective class instead of huge conference sponsored by ESPN.

5. Dean Oliver. A few month ago, my friend Chris Ritchie saw my copy of Basketball on Paper on my shelf. He mentioned that Dean Oliver, the author, used to work at his environmental engineering firm and had even interviewed him before leaving for the sports world. Oliver is now part of the analytics group at ESPN, and I told him this story at the conference. He didn’t remember Chris, but he certainly remembers taking time off to write Basketball on Paper, the seminal work on basketball analytics. Dean showed genuine interest in The Power Rank, particularly how our college basketball rankings contrasted with their own Basketball Power Index.

4. Chad Millman. This author of The Odds and the gambling blog on ESPN recently became editor of ESPN The Magazine. He’s done an amazing job with improving the writing in the magazine. The recent analytics issue features excellent stories on how Brandon McCarthy used analytics to save his pitching career and marry a model (see her on the cover) and how Floyd Fielding earns six figures as an old fashion bookie. I caught Chad in the hallway and complimented him on this work at the magazine. He asked for more features on The Power Rank. They’re coming, Chad. Look for a makeover this upcoming college football season.

3. The NFL’s sophisticated technology. The NFL tracks who takes the field on every play. To do this, they have people take pictures from a few different angles around the stadium. The images are transferred to a human team that writes down the identity of each player by hand. Are you kidding me? Commissioner Goodell, there’s been a recent invention called the personal computer. It sits on your desk and can analyze images. Some smart people in Silicon Valley have already started applying this invention to sports video analysis.

2. Training the German national team. At last year’s conference, Mark Verstegen of Athletes’ Performance was on the opening panel that discussed the 10,000 hours in developing an elite athlete. Only later did I learn that Verstegen was the man Jurgen Klinsmann hired to train the German national team before the 2010 World Cup. I’ve always been interested in the fuzzy, nonscientific side of athletic performance. For example, Born to Run by Christopher McDougall discusses how happiness and joy carries the Tarahumara Indians of Mexico through hundreds of miles of running. Verstegen also believes in this fuzzy side of athletic performance, as his team worked with the Germans to build a culture that supported their fitness program. I didn’t get many details, but elite athletic training is more than just science.

1. Shaking hands with Bill James. This conference would not exist with Bill James. Enough said.

For more content, follow The Power Rank on Twitter.

Related Content:

—College basketball rankings.
—Can a defense force turnovers?
—College football’s incredibly slow progress towards a playoff.

Filed Under: Baseball analytics, Bill James, Billy Beane, College Basketball, Football Analytics, International Soccer, Sabermetrics, Sloan Sports Analytics Conference, Sports Wagering

Are the Pirates lucky or good?

By Dr. Ed Feng Leave a Comment

Pirates, luck or skill?
Who will win NL Central?
Watch out for Brewers

The Pittsburgh Pirates have been bad for 18 years. Since they lost to Atlanta in the 1992 NL Championship Series, the Pirates have not won more than half their games in a season. In the previous six full seasons, they haven’t come within 13 games of winning the 81 games required for 0.500 baseball. It’s a remarkable streak even for a small market Major League Baseball team. In contrast, the Steelers have won two Super Bowls and played in two others since 1992, and the Penguins won the Stanley Cup in 2009. Hence, the people of Pittsburgh wear shirts that say “Pittsburgh, City of Champions… and the Pirates”.

However, this season has been different. The Pirates have a 52-47 record through July 24, 2011. Not only might they end 18 years of losing, but Pittsburgh actually leads the NL Central. The baseball gods are teasing Pirates with thoughts of the postseason when just playing 0.500 baseball would be a monumental achievement for this franchise. In a summer in which Steelers stars Hines Ward and James Harrison have made headlines for all the wrong reasons, the people of Pittsburgh live an a bizarre world in which everyone wants to talk Pirates but not Steelers. But before Pittsburgh starts preparing their ballpark for the World Series, let’s ask how they’re having so much success. Remember, the Pirates lost 105 of 162 games last year, pushing that 0.333 winning percentage that no MLB team normally dips below. The Power Rank had them as 1.61 runs worse than the average team last year. This year, the Houston Astros are also pushing the 0.333 barrier but are only 1.17 runs worse than average. So it makes sense to ask whether Pirates are lucky or good.

To answer this question, consider how a team scores runs: batters get on base, and then subsequent batters drive them home. Hence, a team will score more runs if they can cluster their hits and walks in one inning rather than scatter them throughout the game. Now, it’s highly unlikely that teams can control how their hits are distributed in a game. Do hitters focus more intently with a base runner on third? Do they care less with the bases empty? No one has ever found statistical evidence of clutch hitting in baseball. While this research might not convince every baseball fan, we’ll assume a team can’t control when it hits a homer or gives up an RBI double. For each team that hits 283 doubles and 154 homers, some will score 745 runs while others will score 698. Here, we’ll examine a few formulas for the average runs scored given a team’s statistics. The difference between actual runs scored and this average will measure luck.

Not surprisingly, Bill James, the father of baseball analytics, developed one of the first formulas for run creation. The basic idea is incredibly simple: runs are scored by getting base runners and then driving them home. His Runs Created says runs are equal to the number of runners that get on base times the rate at which they’re driven home. The number of base runners is the sum of hits, walks, and other minor events. James said the second rate factor is proportional to the slugging percentage, or the total number of bases (1 base for a single, 4 bases for a homer) per at bat. Over the last ten seasons, Runs Created overestimates the actual runs a team scores in a season by 18.8 runs. Since teams averaged 758 runs a season over this period, Runs Created is off by only 2.5%. Since a team allows runs in the same way in which it creates them, Runs Created can also be used to evaluate how a team’s pitching and defense prevent runs. Over the same time period, Runs Created overestimates runs allowed by 18.5 runs, or 2.4% error.

However, the Runs Created formula isn’t perfect. Take the simple example in which a batter hits a solo home run in his only major league at bat. The formula of Bill James gives 4 runs created, which clearly overestimates the one run from the homer. In an alternative run creation formula developed by Dave Smyth, home runs explicitly count as one run. The remaining runs come from a second contribution inspired by the simple logic of Runs Created except that home run hitters no longer count as base runners. The basic version of this formula is reminiscent of good physics research in that one attempts to explain complex phenomena with simple expressions. In physics, the merits of these simple expressions were judged by their correctness in particular limits. In baseball, this limit is the hypothetical team which only hits home runs. One can show that Base Runs passes this test. Moreover, Base Runs overestimates runs scored and allowed over a season by 7.5 and 7.2 runs respectively, giving less than 1% error. We’ll use this formula in assessing luck in Major League Baseball.

This season, the Pirates have given up 36 less runs than expected. To explain the magnitude of this luck, the standard deviation of Base Runs from the actual runs is about 14 runs at this point in the season. In other words, one expects that the runs allowed by two thirds of MLB teams deviates from the Base Runs prediction by less than 14 runs. Pittsburgh is more than two standard deviations away from this average on the lucky side. Since The Power Rank considers the actual scores of games, our rankings do not account for this kind of luck. Hence, Pittsburgh should probably be lower than 18th. Here, we list the luck factor on offense and defense for all teams this season. Pittsburgh’s NL Central foe Milwaukee also is an outlier, giving up 16 more runs than expected. The Brewers have been unlucky in run prevention, although they are still deadlocked with Pittsburgh for the division lead. Don’t be surprised to see the Brewers surge and take the division.

Rankings through July 24, 2011:
1. Boston, 62-37, 1.33
2. New York Yankees, 59-40, 1.22
3. Philadelphia, 64-36, 0.88
4. Texas, 58-44, 0.71
5. Toronto, 51-51, 0.40
6. Tampa Bay, 53-47, 0.40
7. Atlanta, 59-43, 0.39
8. Los Angeles Angels, 55-47, 0.29
9. Cincinnati, 50-51, 0.18
10. Detroit, 54-47, 0.13
11. St. Louis, 53-48, 0.12
12. New York Mets, 50-51, 0.10
13. Chicago White Sox, 49-51, 0.03
14. San Francisco, 59-43, 0.01
15. Cleveland, 51-48, 0.01
16. Arizona, 55-47, -0.04
17. Oakland, 44-57, -0.08
18. Pittsburgh, 52-47, -0.10
19. Washington, 49-52, -0.13
20. Milwaukee, 54-49, -0.14
21. Colorado, 48-54, -0.16
22. Seattle, 43-58, -0.31
23. San Diego, 44-58, -0.40
24. Kansas City, 42-59, -0.40
25. Florida, 49-53, -0.43
26. Minnesota, 47-54, -0.53
27. Los Angeles Dodgers, 45-56, -0.54
28. Baltimore, 40-58, -0.80
29. Chicago Cubs, 42-60, -0.99
30. Houston, 33-68, -1.17

Filed Under: Major League Baseball, Pittsburgh Pirates, Runs Created, Sabermetrics

Luck in Major League Baseball

By Dr. Ed Feng 4 Comments

Recently, we discussed how Runs Created and Base Runs can be used to estimate luck in baseball. Here are the results for all teams through July 23, 2011.

Luck in run prevention:
ARI has given up -1.23469494633 less runs than expected
ATL has given up -3.62709441451 less runs than expected
BAL has given up -9.11289297884 less runs than expected
BOS has given up 20.6822005375 more runs than expected
CHC has given up 11.2515196233 more runs than expected
CHW has given up -0.436809763437 less runs than expected
CIN has given up -16.0975518601 less runs than expected
CLE has given up 2.83674501135 more runs than expected
COL has given up -8.35812534013 less runs than expected
DET has given up 17.1942251813 more runs than expected
FLA has given up 5.96916287428 more runs than expected
HOU has given up 4.94569383653 more runs than expected
KCR has given up -16.7197652258 less runs than expected
LAA has given up -28.6712041637 less runs than expected
LAD has given up -1.87349489076 less runs than expected
MIL has given up 16.2299935099 more runs than expected
MIN has given up 9.77368625062 more runs than expected
NYM has given up 3.56993614839 more runs than expected
NYY has given up -18.665513587 less runs than expected
OAK has given up 3.08359511913 more runs than expected
PHI has given up -30.7048037442 less runs than expected
PIT has given up -36.2546001996 less runs than expected
SDP has given up -18.0496995701 less runs than expected
SEA has given up 9.5135694625 more runs than expected
SFG has given up -3.70363003481 less runs than expected
STL has given up 2.64330956604 more runs than expected
TBR has given up -4.620950369 less runs than expected
TEX has given up 6.06547492118 more runs than expected
TOR has given up -0.862096798709 less runs than expected
WSN has given up -20.6411033439 less runs than expected

Luck in run production:
ARI has scored 4.59817863413 more runs than expected
ATL has scored -3.84390920899 less runs than expected
BAL has scored -26.5372500828 less runs than expected
BOS has scored -10.7604018725 less runs than expected
CHC has scored -14.5722967519 less runs than expected
CHW has scored -8.17391509568 less runs than expected
CIN has scored 10.6377091377 more runs than expected
CLE has scored 13.1857474044 more runs than expected
COL has scored -3.54015914982 less runs than expected
DET has scored -17.55991094 less runs than expected
FLA has scored -10.6324611979 less runs than expected
HOU has scored -12.9865003902 less runs than expected
KCR has scored -0.866987671373 less runs than expected
LAA has scored -30.3350973854 less runs than expected
LAD has scored -19.1635769607 less runs than expected
MIL has scored -7.41465249876 less runs than expected
MIN has scored 19.3868500361 more runs than expected
NYM has scored -12.7876028756 less runs than expected
NYY has scored 13.8893535217 more runs than expected
OAK has scored 1.27552447552 more runs than expected
PHI has scored 7.21185540044 more runs than expected
PIT has scored 6.71795645356 more runs than expected
SDP has scored 0.74091295945 more runs than expected
SEA has scored -2.76855859867 less runs than expected
SFG has scored -13.8758209614 less runs than expected
STL has scored -6.83208792351 less runs than expected
TBR has scored -2.95337872315 less runs than expected
TEX has scored 0.204966826497 more runs than expected
TOR has scored 9.57253706526 more runs than expected
WSN has scored 6.71034733652 more runs than expected

Filed Under: Major League Baseball, Sabermetrics

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