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An Analytical Study to Determine the Best Wing Defender in the NCAA by Dominic Symangy


To our viewership, every once so often we come across some good level research and so much so to the point that we feel we need to give it a shot to have it seen publicly.

This study conducted by Dominic Symangy

Here is his work:

An Analytical Study to Determine the Best Wing Defender in the NCAA.


In today’s game of basketball, the value of three-point shooting continues to rise. Due to this, individual perimeter defense is of great importance to a team in order to combat this phenomenon. However, as there many factors behind both team and individual defensive success, it can be hard to quantify it. Therefore, this study will provide a new metric to determine the best wing, or perimeter defenders in the NCAA based on current 2019-20 statistics (as of 1/27/20).


 In order to answer this question, I utilized the college basketball season finder tool provided by to manually pull individual player defensive statistics from the current 2019-20 season. I set a few statistical requirements that a player must meet in order to be included in the dataset. These are as following:

  1. Position = G (Guard)
    1. Reasoning: I chose the position of guard as it most accurately represents the pool of players that are considered “wings”. Although a few players that could be considered “wings” may have been falsely located under the position “forward”, guards provided the most complete collection of “wings.” Ideally, it would have been beneficial to have players classified under the position PG, SG, SF, PF, and C. In today’s game, most “wings” are either shooting guards (SG) or small forwards (SF) and the ability to classify players as such in the dataset would have provided a more accurate sample.
  2. Games played > 12 games
    1. Reasoning: At the time of the manual data scrape, all college teams had played at least 18 regular season games. Therefore, I decided that players who have played at least 12 games, or a majority (two-thirds) of the season’s games, would provide an ideal sample.
  3. MPG >= 20
    1. Reasoning: Similar to the reasoning of games played, the requirement of each player averaging >= 20 MPG ensures that they are on-the-court in at least half of the minutes of in a game. This narrows the dataset to players who are contributing in a significant role for their respective teams.
  4. Defensive Win Shares >= 0.1
    1. Reasoning: According to, defensive win shares are “an estimate of the number of wins contributed by a player due to his defense.” The requirement of >= 0.1 ensures that the player is providing a positive contribution on the defensive end according to the metric’s formula.
  5. Defensive Box/Plus Minus >= 0.1
    1. Reasoning: According to, defensive box plus/minus is “a box score estimate of the defensive points per 100 possessions a player contributed above a league-average player, translated to an average team.” Similar to defensive win shares, by placing the requirement >= 0.1 guarantees the player is providing a positive defensive contribution according to the metric’s formula.

 In order to most accurately quantify which NCAA player has been the best wing defender, I used data from, Microsoft Excel, and RStudio to create my own formula. This metric, called “DomRtg”, incorporates six different advanced metrics provided by that I believe are essential for wing defenders. These metrics are:

  1. DRB% (Defensive Rebounding Percentage)
    1. Definition: An estimate of the percentage of available defensive rebounds a player grabbed while on the floor.
  2. STL% (Steal Percentage)
    1. Definition: An estimate of the percentage of opponent possessions that end with a steal by the player while he was on the floor.
  3. BLK% (Block Percentage)
    1. Definition: An estimate of the percentage of opponent two-point field goal attempts blocked by the player while he was on the floor.
  4. DWS (Defensive Win Shares)
    1. Definition: An estimate of the number of wins contributed by a player due to his defense.
  5. DBPM (Defensive Box Plus/Minus)
    1. Definition: A box score estimate of the defensive points per 100 possessions a player contributed above a league-average player, translated to an average team.
  6. DRtg (Defensive Rating)
    1. Definition: Points allowed per 100 possessions


After determining which metrics to use, I created the formula incorporating each of them. This is described as:

The reasoning behind the formula can be broken down into four parts:

  1. (DRB% + STL% + BLK%)
    1. These three metrics are all based on the basic stats of defensive rebounds, steals, and blocks. Guards, especially wings, are expected to contribute in all three of these areas. However, because not all players play the same number of minutes in a game, basic game stats such as steals, blocks, and rebounders may not be ideal. Therefore, I chose to use these three advanced statistics as they provide a statistic for contributions during time on the floor, instead of per game or per 40 minutes. As they are provided as rational numbers, they are additive and the higher the player’s number, the better a defender they would be considered according to these three metrics.
  2. (DWS + DBPM)
    1. Similar to the first part, the addition of defensive win shares and defensive box plus/minus allows us to place a value on each player. Also similar to part 1, players with a higher additive score represent better defensive players.
  3. (– DRtg)
    1. The subtraction of the defensive rating statistic adds another layer to the overall formula which provides a more accurate representation of the best “wing” defender. Opposite of offensive rating, a lower defensive rating correlates to a better defender. Therefore, because of the subtraction of a statistic generally in the range of 100, the final formula should provide a negative number. This leads to part 4.
  4. Absolute Value
    1. Due to the formula producing a negative number, taking the absolute value of the whole formula ensures the final statistic is positive, which leads to easier interpretation.

DomRtg provides a rating for individual defensive contributions. In the same manner of the defensive rating statistic, those players with the smallest statistic are considered the premier “wing” defenders in the NCAA this season.


 The snippet below from RStudio provides a look at the top 10 defenders this season who were classified as guards. According to DomRtg, Tyler Bey of Colorado, has been the best wing defender this season in the NCAA. An interesting observation to note is that his rating is over 13 points less than Braxton Key at #2, which is greater than the 9-point difference between all players ranked #2-#10. This further solidifies Bey’s ranking at #1 and his dominance on the defensive end as of January 27th, 2020.

As Tyler Bey is junior, I pulled the same data from last year’s NCAA season to determine if he has had continued success on the defensive end. As seen below, this output once again confirms he has been the NCAA’s premier defender for the past two years. Interestingly, he was ranked above Matisse Thybulle of Washington, who was named Naismith Defensive Player of the Year and picked 20th by the Philadelphia 76ers in the 2019 NBA Draft. According to, Bey is predicted to be picked 33rd by the New York Knicks. It will be interesting to watch how Bey’s rest of the season develops and how his draft process goes as he is clearly valuable on the defensive end.

Future Implications

Although this analytical study has provided, in my personal belief, a fairly accurate metric to determine the best wing defender in the NCAA, there are a few constraints to the overall process of producing the formula, DomRtg:

  1. Positions Mislabeling
    1. The first barrier I ran into during the data retrieval process was the fact that players are not labeled as “wings” in box scores or statistical reports. Traditionally, players are group by guard, forward, or center or individually as PG, SG, SF, PF, or C. In this case, the data set included players classified as “guards”. Due to this, some players that fit the wing mold may have been left out as they were labeled as a forward. The ability to use both shooting guards (SG) and small forwards (SF) may have provided a pool of players that better represented the label of a “wing”. However, unfortunately,’s season finder index did not label the players on their specific position but rather as guard, forward, or center.
  2. Defensive Win Shares
    1. While defensive win shares can help decipher the best defensive players, it can become inflated for players on elite defensive teams. According the, “win shares is a player statistic which attempts to divvy up credit for team success to the individuals on the team.” Although it provides a statistic for an individual player, it may correlate with team success. Therefore, a player’s “DomRtg” may be influenced by their team’s success.

While this metric gives a value to wing defenders, it is not the end-all answer to who the best in the NCAA is. However, this metric, combined with others forms of information and analysis, can make personnel decisions, such as scouting, easier and more transparent in the future.

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