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Analytics Magazine

Basketball genomics

November/December 2012

Evaluation of performance: Evolution of the official box score.

The evaluation of performance in basketball analytics parallels the application of methodology utilized in the analytics of genomics.
The evaluation of performance in basketball analytics parallels the application of methodology utilized in the analytics of genomics.

William CadeBy William Cade

Jim Larranaga“When a player looks at the game, they begin with the least important statistic and that’s playing time. … They think that if they play a lot of minutes then everything will work out in their favor … not just our guys, but I think players across the country think it’s just all about playing time. ‘If I play a lot, then I’ll play well …and well, you have to earn that!”
– Jim Larranaga (LEFT), head coach, University of Miami men’s basketball team

What if basketball analytics could formulate an “end-all” value that could justly evaluate team and/or player performance? Regardless of the complexity of its formulation, those immersed in the world of basketball analytics are challenged with this mission: to translate a game of interdependent factors into simple measures of player and team performance. The “Four Factors of Basketball Success,” established by basketball statistician pioneer Dean Oliver, have long played a role in the understanding and the evaluation of team success ( Simply put: If you’re better than your opponent at making field goals, creating turnovers, grabbing rebounds and getting to the foul line, then you’re going to win many more games than you will lose.

Genetics is the study of the variations between humans and how those variations are passed through a family. Described as the “cook book of recipes” that tells our body how to grow and how to develop, DNA is the basis of genetics. In genomics, team research is conducted to investigate the complex instructions between multiple environmental and genetic risk factors.

Interestingly, the same advanced statistical methods implemented to discover and map the genes responsible for disease in families and populations, equivalently can be modified to identify and evaluate the “basketball DNA” genes attributed with success on a basketball team. In principal, the evaluation of performance in basketball analytics parallels the application of methodology utilized in the analytics of genomics. So then, what is your favorite team’s basketball DNA?

“Whereas a coach looks at, playing well … earn your playing time
by your performance on the court … by how well you defend, how
well you rebound, how well you guard your man, how well you run
the floor, how well you make good decisions on offense (make your
shots, make your passes correctly, don’t turn to the ball over) …”

– Jim Larranaga

A fundamental building block for the measure of team performance is time, as function of minutes (“playing time”). Within the analytics of “basketball genomics,” the genetic makeup of playing time is sequenced and coded as a “possession.” How much information observed and collected on a particular possession is fundamental in identifying the basketball DNA associated with/within a team (player and/or lineup).

The Official Box-Score (play-by-play), for example, significantly serves as an invaluable guide in understanding the analysis of the game on a fundamental level. With an unbiased precision, the composition of the Official Box-Score is two-fold. It provides a quarter-by-quarter description of events, along with descriptive measurements, used to inform how well or how poorly a player and/or team have performed. Statistics included in an Official Box-Score are field goals made and field goals attempted (“FGM” and “FGA”), three-point field goals made and three-point field goals attempted (“3PM” and “3PA”), free throws made and free throws attempted (“FTM” and “FTA”), offensive rebounds (“OR” or “OREB”), defensive rebounds (“DR” or “DREB”), total rebounds (“TREB”), assists (“A” or “AST”), steals (“S” or “STL”), blocked shots (“B” or “BS”), personal fouls (“F” or “PF”) , Turnovers (“TOV” or “TO”), minutes (“M” or “MIN”) and points (“P” or “PTS”). Validated by the aforementioned play-by-play component of the Official Box-Score, the methodology for the evaluation of player and/or team lineup performance is displayed in the truncated example of game charting shown in Table 1.

Table 1: Game charting.
Table 1: Game charting.

With the use of advanced statistical software tools (SAS Version 9.3), I have extended the Official Box-Score and established a never-ending framework that can measure the offensive and defensive prowess for a basketball team, by lineup (per game, seasonally, etc.), entitled “Official Box-Score DNA.” The principal areas of extension within Official Box-Score DNA include possession, field goal, rebound and free throw. Derivative and unique (*) statistics provided in Official Box-Score DNA are two-point field goals made and two-point field goals attempted (“2FGM” and “2FGA”), “offensive and defensive possessions (“OP” and “DP”), three-point field goal attempted offensive rebounds (“O3REB” and “O3RB”), three-point field goal attempted defensive rebounds (“D3REB” or “D3RB”), free throw offensive rebound (“FTOREB” or “FTORB”), free throw defensive rebound (“FTDREB” or “FTDRB”) and potential free throws (“PFT”).

Subsequent basketball analytics executed with the utilization of advanced statistical software tools produces measurements of frequency, efficiency and precision relative to team performance. Complimentary to the notable “plus/minus” basketball statistic that looks at the point differential when players are both in and out of the game, the example in Table 2 illustrates team lineup performance for the entirety of a single basketball game.

Table 2: Team lineup performance
Table 2: Team lineup performance.

This impartial approach to quantify team chemistry clearly identifies the qualities shared by players whose play on the court seems simply to flow. It can look at a variety of combinations of players on the court and clearly show which combinations have the biggest effect – best impactful two-player, three-player and even five-player combinations for each game.

For example, suppose your favorite team’s opponent(s) is entering a tournament or playoff game setting, and game preparation involves exact knowledge of successful team lineup defensive performance? Game strategy and decision-making to implement the best team defense would naturally lend itself to some of the following questions: What lineup has played the most defensive possessions together? What lineup defends the two-point field goal attempt the best? What lineup rebounds the three-point field goal attempt the best? What lineup fouls the least? The solution to these defensive questions of interest, respective to game preparation, are illustrated in Table 3.

Table 3: Team defense.
Table 3: Team defense.

In essence, the collection of information provided by Official Box-Score DNA statistics allows for an efficacious way of showing the best-assembled/best combination of players on the court. Though basketball analytics comes with its limitations and imperfections, the pursuit of the advancement of knowledge of the game further incites ongoing analyses and a penchant for better statistics! ?

William Cade (, who holds a master’s degree in public health, is a senior data analyst at the John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, and an institutional staff member of University of Miami men’s basketball team.

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