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Thursday, February 20, 2014
What's the big idea?

By Ross Marrinson
ESPN The Magazine

Carmelo Anthony
A new metric shows that one in five players are more "selfish" in their shot selection than Carmelo Anthony.

This story appears in ESPN The Magazine's March 3 Analytics Issue. Subscribe today!

CAN'T GET ENOUGH of conventional-wisdom-busting metrics? You've come to the right place. These are the eight finalists for the MIT Sloan Sports Analytics Conference paper of the year. You can find links to each paper at espn.go.com/ssac, then cast your vote for the best one. The fan favorite will be announced at this year's conference, from Feb. 28 to March 1. So what are you waiting for? Time to get into the game.

Pointwise: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data
By Dan Cervone, Alexander D'Amour, Luke Bornn and Kirk Goldsberry

The big idea: You're down with PER. You can rattle off the league leaders in regularized adjusted +/-. Now get ready to embrace "expected possession value," a metric that, for the first time, quantifies the on-court decisions -- passing, dribbling, shot selection -- that result in an end-of-possession event.

And we quote: "Carmelo's shot satisfaction is at the 22 percent quantile of player scores, meaning more than one in five players are objectively more 'selfish' in their shot selections."

+3.48: Chris Paul's expected possession value added over replacement player, the highest mark last season.


The Hot Hand: A New Approach to an Old "Fallacy"
By Andrew Bocskocsky, John Ezekowitz and Carolyn Stein

The big idea: Many statheads have long dismissed the notion of momentum, attributing it to randomness. This study flips the "fallacy" of momentum on its head and states that players who have "exceeded expectations" on recent shots are not only more likely to take the team's next shot, they're also 1.2 percent to 2.4 percent more likely -- conditional on the difficulty of the attempt -- to hit it. So take that, doubters.

And we quote: "Larry Summers even chastised the Harvard men's basketball team for their belief in the phenomenon. Yet, among basketball fans and players, the hot hand is a myth that refused to die."

83,000+ The number of shots analyzed for this study.


The Three Dimensions of Rebounding
By Rajiv Maheswaran, Yu-Han Chang, Jeff Su, Sheldon Kwok, Tal Levy, Adam Wexler and Noel Hollingsworth

The big idea: Every rebound has a "timeline, a life cycle, from the release of the shot to the time it is controlled," this paper postulates. Then, using player tracking data, the authors create metrics for each dimension of the timeline -- positioning, hustle and conversions -- allowing them to identify the players who are best at each.

And we quote: "[Defensively], the three guards who perform significantly above expectation: Avery Bradley, Manu Ginobili and Russell Westbrook."

3.4 Percent Percentage Kevin Garnett ups his defensive rebounding probability with his block-out ability, tops in the league.


Automatically Recognizing On-Ball Screens
By Armand McQueen, Jenna Wiens and John Guttag

The big idea: In today's motion-based NBA, the cornerstone of efficient offense is the pick-and-roll. The first step in its perfect execution, of course, is the screen. So this study defines -- using pattern recognition -- what exactly determines an on-ball screen, from its cast of characters (ballhandler, screener and on-ball defender) to the interactions among them.

And we quote: "Oklahoma City ... focuses on forcing switches by running Westbrook-Durant brush screens on the perimeter."

10: Minimum number of feet an offensive player needs to begin the play away from the ballhandler in order to be considered the screener.


"Win at Home and Draw Away": Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors
By Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue and Iain Matthews

The big idea: This paper reveals that although soccer teams tend to play the same formations regardless of location, they play farther up the field at home, indicating that coaches actively try to win at home and hope to draw on the road.

And we quote: "Even though there was no difference in shooting or passing proficiency, home teams had significantly more shots and goals."

1.61: Average home points per match, compared with 1.10 for away matches.


What Does it Take to Call a Strike? Three Biases in Umpire Decision-Making
By Etan Green and David P. Daniels

The big idea: As if the umps don't already take enough grief ... Analyzing over a million pitch calls, this study finds that an umpire's strike zone contracts in two-strike counts and expands in three-ball counts. The study also finds that umpires are "reluctant to call two strikes in a row."

And we quote: "Despite their professional directive and expertise, umpires err in their decision-making; their mistakes are systematic, sizable and pervasive."

19 percent: Decrease in the probability that an umpire will call a strike on a pitch in the corner of the strike zone with a two-strike count.


Can't Buy Much Love: Why Money is Not Baseball's Most Valuable Currency
By Martin Kleinbard

The big idea: Conventional wisdom holds that payroll inequality in baseball leads to competitive imbalance. One problem: It isn't true. This study says that the variation in wins explained by payroll, or Win Buying Index, is declining relative to history; identifies the most valuable currency in baseball (young, pre-arbitration players); and provides recommendations to current MLB policy to maintain the downward trend.

And we quote: "Young talent, not deep pockets, is the ultimate trump card in the MLB of today and tomorrow."

80 percent: Percentage of the variation in wins among franchises that cannot be explained by payroll.


A Data-Driven Method for In-Game Decision- Making in MLB
By Gartheeban Ganeshapillai and John Guttag

The big idea: One of the most important in-game decisions an MLB manager has to make is whether and when to take out his starting pitcher. To do this, managers often look at pitch counts. This paper establishes a better model, one that can predict whether a pitcher will surrender a run in the next inning.

And we quote: "Our model would've led to a different decision 48 percent of the time from the fifth inning on in close games."

60 percent: Percentage of the time that a starter gave up a run when the model would've suggested putting in a reliever.

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