poster id 10 statistical analysis of predicting nba mvp ...duncan averaged 25.5 points and 12.7...

Poster ID 10 Statistical Analysis of Predicting

NBA MVP Winners Mason Chen

Who should win MVP Awards?

1 © IEOM Society International

2015-2016 KIA NBA MVP Award Result

• Voted by 131 Sports Media Reporters (Cluster Sampling Method)

• Curry got all 131 Votes in 2015-2016 Season (consensus choice) 2 © IEOM Society International

4. Magic Johnson over Charles Barkley, 1990 In the closest NBA vote since the media took over the voting in 1981, Johnson edged Barkley by 22 points, even though Barkley received more first-place votes (38 to 27). Barkley averaged 25.2 points and 11.5 rebounds and shot 60 percent from the field. Johnson averaged 22.3 points, 11.5 assists and 6.6 rebounds (while shooting 48 percent). Barkley's Sixers won 53 games while the Lakers won 63. Oh yeah -- Michael Jordan averaged 33.6 points, 6.9 rebounds and 6.3 assists and finished third in the voting.

7. Tim Duncan over Jason Kidd, 2002 Duncan averaged 25.5 points and 12.7 rebounds for the Spurs while Kidd engineered an amazing turnaround for the Nets after coming over from Phoenix: from 26 wins to 52 and a trip to the NBA Finals. He was attempting to become the first point guard since Magic Johnson in 1990 to be named MVP, but Duncan's scoring won out over Kidd's playmaking. Kidd had averaged 14.7 points, 9.9 assists and 7.3 rebounds, but shot just 39.1 percent (no MVP had shot that poorly since Bob Cousy in 1957).

10. Karl Malone over Michael Jordan, 1997 Jordan had already won four MVP Awards, so maybe voters were simply getting a little tired of giving it to him. Jordan had another typical MJ year: 29.6 points, 5.9 rebounds and 4.3 assists. The Bulls, however, did slip from a record 72 wins to 69. Malone averaged 27.4 points, 9.9 rebounds and 4.5 assists in leading Utah to 64 wins. Malone topped Jordan in a close vote (986 points to 957), but Jordan would get the last laugh: the Bulls beat the Jazz in six games in the NBA Finals.

Most Controversial MVPs in Sports (Cluster Sampling Questionable)

http://www.espn.com/page2/s/list/MVPcontroversy.html 3 © IEOM Society International

Literature Research: Oddsshark’s Prediction

http://www.oddsshark.com/nba/nba-mvp-201617-betting-odds

Early Season Prediction Can we match of even beat their MVP prediction?


Build Predictive Model (Function)

2003-2016 Team Record

(X)

2003-2016 Player Statistics

(X)

MVP Model Y= F(X)

2016-2017 Team Record

(X)

2016-2017 Player Statistics

(X)

Predict 2016-2017

MVP Winners (Y)

Actual 2003-2016

MVP Winners (Y)


http://www.foxsports.com/nba/stats?category=SCORING

Measure: Collect Player Statistics Data

Y (MVP Index, Ranking) = Function (Player Statistics, Team Record)

© IEOM Society International

Measure: Collect NBA Team Record

http://www.basketball-reference.com/leagues/NBA_2016_standings.html



Add Four Combining Statistics (Interaction Effect, Trim Insignificant Terms)

In addition to original raw data, authors have taken SME opinions and created four Combining Statistics: (1) RB/MIN, (2) AST/MIN, (3) A/T Ratio, (4) PPG/MIN which can demonstrate offense & defense efficiency



Data Transformation to Z-Scale N(0,1)

• Z-Transformation will transform each individual players’ statistics category to normal Z scale for direct comparison (eliminate sample mean and sample standard deviation Bias)



Create MVP Index and Uniform Model

• Create MVP Index: by summing each Z-score of all statisticscategories (Uniform Model)

Y (MVP Index, Ranking) = Uniform Function (Player Statistics, Team Record)


Root Cause Analysis of Uniform Model

Some correlations based on Uniform Model, but not very strong prediction between MVP Index (prediction) and Actual MVP Rank


Root Cause Analysis of Uniform Model

• Some player Statistics are more relevant than others on MVP Rank (How to Quantify Significance?)

No Significance

Certain Significance


Actual Variable Rank Median GP-N 1 0.712 2 0.635 3 0.443 4 0.405 5 0.520 * 0.3283 Min-N 1 0.391 2 0.318 3 0.306 4 -0.141 5 0.391 * -0.0205 FG%-N 1 0.814 2 0.776 3 0.446 4 0.369 5 0.291 * -0.1157

3pt%-N 1 0.409 2 0.443 3 0.145 4 0.024 5 0.029 * 0.2415 FT%-N 1 0.245 2 0.595 3 -0.049 4 0.046 5 0.415 * 0.1689 RB-N 1 0.404 2 0.476 3 0.422 4 0.549 5 -0.268 * -0.2861 AST-N 1 1.041 2 0.282 3 0.211 4 0.282 5 0.827 * -0.3335

Weighted Model

• Use descriptive statistics (median, range) of top two ranked players vs. non-ranked players to quantify the Weight Factors

• Weight coefficients will be added to the Uniform Model becoming the Weighted Model


Minitab Weighted Model

Identify the most critical Player Statistics Categories and add the Weight Coefficients to create Weighted Model

Top 5 Z_Variables:

1. Point per Minute

2. Point per Game

3. Field Goal %

4. Assist per Game

5. Rebound per Game


Minitab Best Subset (Feature Selection) Simplify the Model, Enhance SNR

Top 5 Variables:

1. Field Goal %

2. 3-Point %

3. Rebound per

Game

4. Games Played

(Healthy, Durant)

5. Point per Minute


Weighted Model

Weighted Coefficients

1. Point per Minute

2. Point per Game

3. Field Goal %

4. Assist per Game

5. Rebound per Game

Best Subset Selection

1. Field Goal %

2. 3-Point % (Curry)

3. Rebound per Game

4. Games Played (Healthy)

5. Point per Minute

This Best Subset algorithm can minimize the Multi-Collinearity (shown

in Mallows Cp coefficient) among the linearly dependent factors.

Inflated Power VIF > 5


Model Accuracy: Uniform vs. Weighted

Weighted Model has slightly improved the model accuracy


Power Model (Consider Team Record)

• Both Uniform and Weighted Models considered PlayerStatistics only

• MVP is the Most Valuable Player. Therefore, player’scontribution to the team record should be considered

• Majority of the past MVP winners are from the Best orBetter Teams

• Model should set Team Record as a higher priority abovethe Individual Statistics Performance (Power > 1)

• Created a new Power MVP Index= Weighted Index *(Team Winning%) ^ (Power)

• Individual Weighted Model= Power Model (Power= 0)18 © IEOM Society International

Optimum Power Model (Prevent Over-fit)

Power= 3 model has shown 70% Prediction Accuracy

Power= 3 Optimum

Power= ∞ Let the best team decide

their own MVP (even more subjective)?

19

Weighted

Player Statistics ~ Team Record


Data Mining Discriminant Model • Can we apply the Data Mining Discriminant (Similarity) Algorithm to

predict the MVP Winners? • Then, calculate the correct proportion between the actual winners

and the predicted winners. • Without including the Team record Factor, Discriminant Model did

beat the previous Uniform Model and Weighted Model

Discriminant Weighted Uniform


Predict 2017 MVP (April.10 Update)

Around mid April after completed the regular season, we will predict MVP winners two moths earlier than official NBA announcement


poster id 10 statistical analysis of predicting nba mvp ...duncan averaged 25.5 points and 12.7...

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