konakalab
December 06, 2022
200

Proposal of offense-defense WPA metrics in B.LEAGUE

Presentation sildes for ASCC2022 (https://acss.ear.com.sg/)

Absgtract: Classical stats recorded in basketball box scores of matches attribute contributions to the player who handled the ball last in the play. Therefore, it is difficult to quantify each player’s defensive contribution using classical stats.
Play-by-play data is recorded and disclosed in several professional basketball leagues. Advanced metrics based on this data have been extensively investigated to quantify each player’s contributions to their team’s victory. This study focuses on win probability added (WPA). In basketball, WPA can
be defined as the difference between the predicted winning ratio before and after a single play.
The probability difference is equally distributed among all players on the court. WPA can be calculated separately in offense and defense situations.
In this study, play-by-play data disclosed by B.LEAGUE, a men’s professional basketball league in Japan, was collected and analyzed. In the first step, we developed a predicted win probability model that guarantees monotonicity on the score difference. Next, we calculated the offense/defense WPAs
and their time averages. These metrics quantified each player’s contribution to winning. Moreover, they expressed player features such as the pace of their plays.

konakalab

December 06, 2022

Transcript

1. Proposal of offense/defense WPA metrics in B.LEAGUE Koji SUGIE*, Eiji

KONAKA (Meijo Univ. Japan)
2. Outline Background STATS in basketball Impact metrics Proposed method Construction

of consistent win probability model Main proposal: Offense/Defense Win Probability Added Numerical experiment Summary and future work
3. Background: stats in basketball  (Classical) stats: counts and ratios

of events.  Attributed to the player who touched the ball last.
4. Background: stats in basketball  Detailed play-by-play records  Available

in Japanese professional B.LEAGUE Stats and records can be used to evaluate each player’s contribution
5. Background: contribution evaluation from stats How to evaluate the contributions

of the players who did not touch the ball? https://www.youtube.com/watch?v=qKde9M1gKvY&t=1311s 2022/07/18 accessed
6. Background: impact metrics “Impact metrics (IM)” tries to evaluate each

player’s contribution in single value. Example: +/-, EFF, PIPM,LEBRON,RPM,RAPTOR, … Problem: Simple IM: do not include situation (time, score diff, …) Advanced IM: calculation methods are not fully disclosed in academic context (“magic recipe”)
7. Background: win probability model  How to include situation? 

Time and score difference  Win probability model  Problem  Consistency Large score diff<->Large win prob.  Previous studies: Inconsistent win probability model (Deshpande[2016]) Deshpande[2016] Time[s] Score difference]
8. Research flow and main objective • Construct consistent win probability

model • Contributions to win of all players is evaluated • Numerical evaluation • Japanese B.LEAGUE
9. Outline Background STATS in basketball Impact metrics Proposed method Construction

of consistent win probability model Main proposal: Offense/Defense Win Probability Added Numerical experiment Summary and future work
10. Consistent win probability model with score difference  Win probability

model ෝ 𝑤 𝑡, Δ𝑠  𝑡: time[s] from tipoff  Δ𝑠: score difference  Dataset  B1 League, 1736 matches  2016/9-2019/10 (3 regular seasons)
11. What is “consistency”  Consistent win probability model ෝ 𝑤

𝑡, Δ𝑠  ෝ 𝑤 𝑡, Δ𝑠 is consistent ⇔ ෝ 𝑤 is monotonic as Δ𝑠  Logistic regression is done for 𝑡 = 0, ⋯ , 2400
12. WPA originates in baseball (MLB) Offense WPA(WPA𝑂): sum of the

change of ෝ 𝑤 in offense situation Offense situation=ball possession Defense WPA(WPA𝐷): sum of the change of ෝ 𝑤 in defense situation Defense situation=non-ball possession Total WPA：WPA = WPA𝑂 + WPA𝐷 for each player Definition of WPA (Win Probability Added) Can evaluate the contributions of the players who did not touch the ball
13. Example of WPA O/D ෝ 𝒘 𝒔𝒊 𝒕 𝒔𝒋 ෝ

𝒘 O/D O(Start) 0.65 18 702 9 0.35 D(Start) O(end) 0.72 21 722 9 0.28 D(end) D(start) 0.72 21 722 9 0.28 O(Start) ・ ・ ・ WPA𝐷 in Team 𝑗: 0.28 − 0.35 5 = −0.014 WPA𝑂 in Team 𝑖: 0.72 − 0.65 5 = 0.014 ・ ・ ・ Team 𝑖 Team 𝑗 ෝ 𝑤 722,12 = 0.72
14. Outline Background STATS in basketball Impact metrics Proposed method Construction

of reasonable win probability model Main proposal: Offense/Defense Win Probability Added Numerical experiment Summary and future work
15. Numerical experiment 268 players in B1 League, 2018-19 regular season

Following example Teams: Chiba (52 wins) and Tochigi (49 wins) Played more than 10000[s] (about 3[min/match])

17. Discussion and analysis: pace  Team characteristics  WPA/s ~

Points/s  Tochigi has larger WPA𝑂/𝑠 and WPA𝐷/𝑠  Tochigi favored high-paced transition game WPA/s (Pink: Tochigi, Green: Chiba)
18. Discussion and analysis: PGs Team: Chiba Team: Tochigi Togashi Nishimura

Watanabe Ikaruga

high high
20. Discussion and analysis: weakness of bench players ONO Ryumo Bench

player Position: SF/PF Similar WPA𝐷 , but small 𝑊𝑃𝐴𝑂 compared with the regular SF and PF Team: Chiba Ono
21. Summary ☑ • Construct consistent win probability model ☑ •

Contributions to win of all players is evaluated ☑ • Numerical evaluation in Japanese B.LEAGUE
22. Future work ☑ • Construct consistent win probability model New!

• Contributions to win of all lineup will be evaluated • Pairwise comparison model New! • Numerical evaluation in Japanese B.LEAGUE
23. Many thanks! And welcome questions and comments! Sugie, Konaka. “Proposal

of offense/defense WPA metrics in B.LEAGUE”