The newest trend in basketball is the use of advanced statistics not only to evaluate teams but individual players as well. Player Efficiency Ratings strive to drill all the stats a player accumulates down to one number that represents their effect on the game and individual productivity. The amount of statistical information and ratings available on the NBA is mind boggling. If you visit the NBA Stats Glossary you can see all the available stats and ratings. As a coach, I have often utilized player efficiency ratings as a way to motivate and track a player's progress.

Back when I played in the NBA the EFF rating was used the most and often included on the box score. The NBA's Efficiency Rating is a single number measure of a player's overall contribution (both positive and negative) to a game he plays in. It is calculated as follows: (Points + Rebounds + Assists + Steals + Blocks) - ((Field Goals Att. - Field Goals Made) + (Free Throws Att. -Free Throws Made) + Turnovers). The biggest problem with the EFF rating is it does not factor in minutes played so therefore measures productivity but not really efficiency. Players who play more minutes are more than likely going to have a higher number. The positive to the EFF is that it is very easy to calculate.

The newest efficiency rating in the NBA is the Player Impact Estimate (PIE). PIE measures a player's overall statistical contribution against the total statistics in games they play in. PIE yields results which are comparable to other advanced statistics (e.g. PER which I discuss below) using the following formula: (PTS + FGM + FTM - FGA - FTA + DREB + (.5 * OREB) + AST + STL + (.5 * BLK) - PF - TO) / (GmPTS + GmFGM + GmFTM - GmFGA - GmFTA + GmDREB + (.5 * GmOREB) + GmAST + GmSTL + (.5 * GmBLK) - GmPF - GmTO). While I have never used PIE with any of my teams I do like that it creates a statistic relative to the game. Basically it is giving you a percentage showing how much of a positive or negative impact a player had on a game.

One of the simplest efficiency stats which is now shown on all NBA box scores is the +/-. The +/- stat looks at point differential when players are in and out of a game, demonstrating how teams perform with various combinations. While I like and use this statistic, I don't feel it truly represents a single player's impact on the game.

If you want to take player efficiency stats to an even more detailed analysis you can take a look at John Hollinger's PER on ESPN. The PER is an extremely complicated formula based on minutes played, pace and other factors. Due to how complicated it is, PER is not a stat coaches outside of the NBA and NCAA can use with their players because it's nearly impossible to calculate.

Because of this, I created a much simpler PER which I oddly enough named "Simple PER". Simple PER is calculated as follows: (2FG Made*2) - (2FG Attempted*.75) + (3FG Made*3) – (3FG Attempted*.84) + (FT Made) - (FT Attempted*-.65) + Rebounds + Assists + Blocks + Steals - Turnovers. Similar to PER, the Simple PER only gives positives for scoring if you shoot a decent percentage (see picture for breakeven shooting percentages). If a player scores a ton of points but shoots a very low percentage then they would not have a positive Simple PER. I chose a relatively low breakeven shooting percentage but that can be adjusted as needed.  I like this formula because it is simple to calculate but factors in shooting percentages.

No player efficiency rating is perfect because it is practically impossible to drill down a player's impact into one number. However, these formulas and numbers if used consistently can be a great way to evaluate, motivate and educate players on the game and their impact on helping the team win.

The goal of every college team is to be playing in the NCAA Tournament this time of year. Being on the national stage as one of the “best” 68 teams in the country has obvious benefits for not only the basketball program but university as a whole. Coaches today have a wealth of statistics and resources to help evaluate their team and opponents.  Advanced statistical analysis, such as Dean Oliver’s Four Factors (explanation here), is readily available at sites like kenpom.com for all to see and parse through.

With this wealth of data, the hard part is figuring out how to apply it when building a team and formulating game plans. Per Dean Oliver’s research, the premise is that offensive and defensive eFG% has the most impact on winning. The next most influential stats are TO%, OR% and Free Throw rate.  We have heard coaches at all levels talk about taking good shots, eliminating easy shots for the opponent, not turning it over and rebounding the ball. In a nutshell that’s what these stats measure, but in a more intricate and detailed way.

In evaluating the Four Factors for any team, we must remember that being good in these areas does not guarantee wins. It can however be an excellent predictor of success. In addition, while the overall rank of each individual statistic is important, the differential between offensive and defensive numbers can be more indicative of success. We can’t look just at eFG% offense without also comparing that to eFG% defense. 

When evaluating the eFG% stats and differential of NCAA teams here are some facts:

• 28 of the top 68 offensive eFG% teams are in the NCAA tournament (41.2%)
• 30 of the top 68 defensive eFG% teams are in the NCAA tournament (44.1%)
• 39 of the top 68 differential teams (off eFG% - def eFG%) are in the NCAA tournament (57.4%)
• 6 of the 68 team NCAA field had a negative eFG% differential

The fact that jumped out to me was the last one in regards to the NCAA field only having six teams with negative eFG% in the tournament. When you look deeper, it gets even more interesting. The teams in the negative were Colorado, Providence, Milwaukee, Wofford, Mount St. Mary’s and Cal Poly. Out of those 6 only two, Colorado & Providence (bubble team), would have made the tournament without being an automatic qualifier. The interesting statistic for both of those schools is that they both have very positive OR% differential: Colorado +7.9 and Providence +4.7.  One could argue that they make up for the eFG% disparity by consistently winning the battle of the back boards.

Out of curiosity I also looked back at the NCAA fields for 2013 and 2012. In the NCAA field for 2013, only Illinois and James Madison had negative eFG% differentials. While both of these teams had slight negatives in rebounding differential, they both had positive TO% differentials: Illinois +4.3 and James Madison +3.9. Back in 2012, the field contained six teams with negative eFG%: Southern Miss, West Virginia, Loyola MD, Detroit, Lamar and W Kentucky. Only two of these teams, Southern Miss and West Virginia, were at large bids. Similar to 2014 they both have high positive OR% differentials: Southern Miss +7.5 and West Virginia +10.5. 

So in conclusion, eFG% differential does not guarantee enough success to make the NCAA field, it is highly unlikely to have a negative differential and make it in. If your team doesn’t consistently win the eFG% stat, you better be able to rebound at a high rate or take care of the ball. Regardless of the stats, you can always make a run in the conference tournament to go dancing!