Predict NBA 2016-2017 Regular Season MVP Winner

Mason Chen, Black Belt, Stanford OHS

Who should win MVP Awards?

1

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

4. Magic Johnson over Charles Barkley, 1990In 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, 2002Duncan 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, 1997Jordan 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(Questionable Cluster Sampling)

http://www.espn.com/page2/s/list/MVPcontroversy.html 3

Build Predictive Model (Function)

2003-2016 Team Record

(X)

2003-2016 Player Statistics

(X)

MVP ModelY= F(X)

2016-2017 Team Record

(X)

2016-2017 Player Statistics

(X)

Predict 2016-2017

MVP Winners (Y)

Actual2003-2016

MVP Winners (Y)

4

• Use historical data (2003-2016) to build the MVP Model• Use 2016-2017 player statistics to predict the new MVP Winners

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

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 both offense & defense efficiency

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

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

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

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

6

Create MVP Index and Uniform Model

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

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

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Evaluate the Uniform Model

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

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Root Cause Analysis of Uniform Model

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

Not Significant

Certain Significance

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ActualVariable Rank MedianGP-N 1 0.712

2 0.6353 0.4434 0.4055 0.520* 0.3283

Derive 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

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Top 5 Factors

1. Point per Minute

2. Point per Game

3. Field Goal %

4. Assist per Game

5. Rebound per Game

Best Subset (Feature Selection)Enhance the Weighted Model

Top 5 Variables:

1. Field Goal %

2. 3-Point %

3. Rebound per

Game

4. Games Played

(Healthy, Durant)

5. Point per Minute

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Healthy Durant was leading the MVP race before being injured in the mid season (Durant won Final MVP)

Compare two Weighted Methods

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

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Model Accuracy: Uniform vs. Weighted

Weighted Model has slightly improved the model accuracy

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Power Model (Consider Team Record)

• Both Uniform and Weighted Models considered Player Statistics only

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

• Majority of the past MVP winners are from the Best or Better Teams

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

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

• Individual Weighted Model= Power Model (Power= 0)14

Collect NBA Team Record

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

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

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Optimum Power Model(Prevent Over-fit)

Power= 2 model has shown 67% Prediction Accuracy

Power= 2Optimum

Power= ∞Let the best team decide

their own MVP (even more subjective)?

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Weighted

Player Statistics ~ Team Record

2016-2017 Regular Season MVP

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

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Co-MVP (Kobe Bryant’s Suggestion)?

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Team Performance is more important (Power >1.5)

Individual Performance is more important (Power < 1.5)

The Winner is Westbrook (released on June 26, 9PM ET, 2017)