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Explanation of NEP
| To Stat or Not to Stat
If you are reading this, you most likely have some degree of interest in statistics and the analyses of statistics. A number of you have emailed me asking for an explanation of some acronym or another or the pertinence of a certain stat. I thought I would use this edition of ITN (Inside the Numbers) to answer some of these questions and give an overview (or perhaps I should say my point of view) of statistical analyses.
The first and most important aspect of statistical analysis is to determine what it is you want to accomplish. Roy Williams uses stats to compliment his understanding of the performance of his team. He also uses stats to use as ammunition for motivating his players. He will review the stats of upcoming opponents to get a feel for the type of game they play and determine their strengths and weaknesses. I’m quite sure there are a myriad of other uses Roy (and other coaches) has for various stats.
As spectators we may view stats from an entirely different perspective. We can use them as purely a source of entertainment. An example would be looking at the shooting percentage or assists for the 2001-2002 season and noting that KU led the nation in both categories. We really don’t want to go into it any deeper we just like knowing fun facts about our team and how our team compares to others in the conference or in the NCAA.
We can use stats to fulfill our latent desire for trivia. It is in our God created nature to desire knowledge and trivia is an especially fun type of knowledge. Many of us like studying and knowing all sorts of stats just for the sake of knowing them. We may spend a great amount of time discussing basketball with our buddies, at the office or whatever. We are not really too concerned with the deeper meaning of the stats but we do like studying them and knowing them.
The third use of stats is to gain a deeper insight into the nuances and strategies of the game. Some of us like to know the “whys,” “whats,” and “hows” of basketball. Much can be understood from watching the game and generally understanding the basic strategies. An astute in-depth analysis of statistics can provide insights not apparent to the more casual observer. These insights are in no wise necessary to enhance our enjoyment of the game. A person who disdains stats of any type has equal capacity to derive enjoyment from spectating as the most ardent statophile. I am NOT hoisting the analysis of stats upon a pedestal and claiming one who is motivated and adept at statistical analysis is in any way more in tune with the game. It is simply another avenue available to bring enjoyment to a fan so inclined to spend the time and energy to do the various analyses.
Once you have decided what you want to accomplish by your review and analysis of the stats you need to know the three golden rules of statistical analysis according to Don. They are:
- Stats must necessarily be complemented by a reasonable, subjective analysis.
This is basically a rule that provides the frame of reference with which to view the stats. Some people use stats in a vacuum of common sense and thus destroy their credibility. We can’t possibly record enough stats to accurately portray all the factors involved in the game. Stats measure a limited number of aspects of the game. They are like the framing for a house. You pretty much know what the shape, size and location of the house is but you can only imagine what the final build out will look like. Of course the shape, size and location are critical aspects of a house but certainly they are not the only attributes of importance. The human mind far exceeds any computer in it’s ability to process certain types of information. If a trained eye, such as Roy Williams’, were to observe a player, he could make a very good judgment of the player's overall ability. Afterward, he may go back and review the player’s statistical history to gauge things like volatility, consistency, and find anomalies, but merely observing will be sufficient for an experienced judge. So therefore when we analyze the stats we should always do so with the perspective gained from an experienced, subjective observation.
- Stats must be viewed in their entirety (i.e. no individual stat tells the whole story). There used to be a TV game show called Name That Tune. The idea was to hear just a few notes of a song and be able to name the song. The contestants were supplied with various clues about the song such as when it was written, who wrote it, or some nuance of the song that was distinctive. After receiving the clues the contestant would have a pretty good idea of what the song was and he would tell the emcee he could , “name that tune is 3 notes” (or whatever number he decided.) His opponent could then bid to name the tune in some fewer number of notes. If he thought he knew the song from the clues, he could choose to name the tune in one note. That is sort of like analyzing statistics. We can look at a few stats and think we can “name that outcome,” or “name that cause.” We may be correct, but we may be dead wrong. To really understand all the nuances of the game and the team and individual performances we have to analyze all the available stats. Of course in keeping with rule number 1 we must also start from a frame of reference formed from experiential observation. One of my biggest pet peeves is when a player scores a bunch of points and gets all sorts of accolades by TV announcers. In reality he may have played a terrible game and scored his points simply because he shot the ball every time he touched it. I believe KU has benefited from this mentality over the years. It seems we tend to run into players who get inordinately pumped up when they play KU on national TV or in Allen Fieldhouse and they go ballistic. Rayford Young (Texas Tech), and Desmond Mason (OSU) come to mind and there have been many others. Their individual performances may have been breathtaking but it is often at the expense of the team game plan and flow. Hence, KU wins in spite of some opposing player having a “career” game. My point is that points scored by itself does not give anything close to an accurate picture of an individual’s performance. What if a player scored 30 points against KU, would you call that a great game? What if he also had no assists, 7 turnovers, was tired and unfocused on defense, and shot 10 for 30 from the field? In this case he probably did more harm for his team than his 30 points did good. No single stat has much validity outside it’s relationship to the entire family of stats.
- Do not be pedantic, dogmatic, or intransigent in your statistical analyses.
This statement sounds pretty pedantic, dogmatic and intransigent in itself, doesn’t it? My point here is to stress that statistical analyses can be manipulated by a deft statistician. Especially the more esoteric derivative-type stats. To keep stats fun and enjoyable, we should always keep an open-mind and not think our view is the end-all to be-all of statistical analyses.
The unofficial fourth Golden Rule is to think the NEP and all related stats are perfect and infallible. I guess it is self explanatory why I did not include that as one of my Golden rules. I suppose the fact that that rule would definitionally violate the other three rules makes it pretty much impossible to include it. Mostly I didn’t include it because it would be disgustingly self-serving. Enough of that.
PRIMARY STATISTICS
Let’s take a look at some of the basics of statistical analysis. First of all let’s recognize the primary stats we generally have available. Following is a list of the primary stats:
| FGM | Field Goals Made
| | FGA | Field Goals attempted
| | 3FGM | 3-point Field Goals Made
| | 3FGA | 3-point Field Goals Attempted
| | FTM | Free Throws Made
| | FTA | Free Throws Attempted
| | Pts | Points Scored
| | OR | Offensive Rebounds
| | DR | Defensive Rebounds
| | TR | Total Rebounds
| | PF | Personal Fouls
| | A | Assists
| | TO | Turnovers
| | Blk | Blocked Shots
| | Stl | Steals
| | Min | Total Minutes Played
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We have each of these stats for each player and in some cases at the team level. The individual stats are summed up for the team and the opponents stats are summed up to get the opponent team stats. These comprise the “PRIMARY” stats. By “PRIMARY” I am referring to raw measured and recorded data.
SECONDARY STATS
Secondary stats are raw data that are not as readily available and not as generally utilized and recognized as primary stats. There is no way to list all of the secondary stats since many coaches have secondary stats he espouses. Roy Williams tracks “floor burns” and Kelvin Sampson tracks something he calls “hustle points.” Some of the more ubiquitous secondary stats are listed below.
| Poss | Possessions (the number of time during a game a team takes possession of the ball)
| | Trips | Number of times a player or team goes to the FT line
| | Charges | Number of times a player takes or commits a player control foul (charge)
| | Travels | Traveling calls on a team or player
| | PPD | Player Point Differential or the sum of the scoring differentials for times a player was in the game.
| | PiP | Points in the Paint - Points scored inside the free throw lane
| | BCP | Backcourt Points - Points scored by perimeter (backcourt) players (usually guards and wing forwards)
| | FCP | Front Court Points - Points scored by post (frontcourt) players (usually centers and non-wing forwards)
| | POT | Points Off Turnovers - Points scored immediately following a takeaway or a turnover by the opponent
| | TrP | Transition Points - Points scored of fast breaks
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As I mentioned there can be all sorts of secondary stats a coach or very dedicated spectator can track. Of the ones mentioned above by far the most pertinent to the analyses I employ is Poss. The Possessions is central to the NEP and other derivative stats I use in my analysis. More on that in a minute.
DERIVATIVE RATIOS
Beyond the primary stats and secondary stats lie the derivative stats. Derivative stats can take on any form and complexity. The simplest and most common derivative stats are the simple ratios. You can calculate any number of ratios limited only by your imagination. Differing ratios have differing value and provide insight to different aspects of the game. Here is a non-exhaustive list of derivative ratios;
| FG% | Field Goal Percentage
| | 2FG% | 2-point Field Goal Percentage
| | 3FG% | 3-point Field Goal Percentage
| | FT% | Free Throw Percentage
| | A/TO | Assist to Turnover Ratio
| | %LoB | Percent Loss of Ball - Percentage of a teams possessions in which they committed a turnover or lost the ball.
| | PPP | Points Per Possession - the total points scored divided by the number of possessions.
| | A/FGM | Percent of made field goals which were assisted
| | Pts/FGA | Points scored per each Field Goal Attempt
| | %Blk | Percent of Shots Blocked - is the percent of FGA’s that were blocked. Can be offensive or defensive.
| | ?/G | Any primary stat per game (i.e. points per game, rebounds per game, assists per game, minutes per game, etc.)
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NORMALIZED STATS
Normalized stats are a special form of derivative ratios. When we speak of normalizing stats we are referring to normalization against minutes played. This simply means prorating the stats to a constant value of minutes played. The resulting “normalized” stat results in a “per minute” or “per 40 minutes” or some other time-constant. The single value of normalized stats is to provide a more valid basis of comparison of two players who may play radically different quantities on minutes. For instance, how can you compare the contribution and performances last year of Wayne Simien to Drew Gooden or Nick Collison when Wayne played only about 15 minutes per game and Nick and Drew played in excess of 30 minutes per game? Wayne’s total numbers will be far below those of both Nick and Drew if for no other reason than he played fewer minutes. By normalizing the stats you can measure the “effectiveness” of a player. If you remember nothing else about normalized stats remember that normalized stats are a measure of the “effectiveness” of a player while he is in the game whereas the un-normalized primary stats measure the overall contribution of a player.
DERIVATIVE STATS
Derivative stats are more complex calculated values utilizing two or more primary or secondary stats as well as various correlations and constants. That’s a fancy way of saying these are often complicated and difficult to understand big old hairy equations. This covers limitless possibilities. I’ll limit my discussion to the derivative stats I utilize. First let me list the main derivative stats you see in my columns. Here is a list:
| NEP | Net Equivalent Points - The NEP is an attempt to distill all primary stats down to a single calculated value that represents a player's overall contribution to the two primary team goals. The two primary team goals (as assumed in the NEP) are to score each possession and to prohibit the opponent from scoring on each of their possessions. When combined this is summed up by saying the primary goal is to outscore your opponent. Statistics are discreetly tied to either defense (i.e. Stl, Blk, DR) or offense (i.e. Pts, A, TO). Therefore the NEP works from the premise of the two primary goals stated above rather than the single goal of outscoring your opponent. The resulting NEP value is the overall contribution of the player in achieving the primary goals.
| | n-NEP | Normalized NEP - Whereas the NEP is the “overall” contribution a player in achieving the two primary goals, the n-NEP is a direct measure of a player's “effectiveness” while he is in the game. Just like the Normalized Stats discussed above, the n-NEP is “normalized” to 40 minutes of play. This stat essentially is used solely for purposes of comparison. It allows us to compare a player’s effectiveness against another player who plays many more minutes or against that same players effectiveness in other games when he played many more minutes. The n-NEP is calculated by prorating the NEP up to 40 minutes of play.
| | NEP Rating | The NEP Rating is an attempt to use the NEP and n-NEP to rate individual players for purposes of comparison. For instance, we would use the NEP Rating if we want to compare all the players in the NCAA or in the Big 12 conference, or even on a team. The NEP rating is a mathematical combination of the NEP and the n-NEP. The NEP measures a player’s overall contribution (most important) and the n-NEP measures a player’s effectiveness while in the game. Both of these ratings have meaning and measure different aspects of the overall performance. An example would be Wayne Simien and Nick Collison last season. Wayne played only about half the minutes as Nick, so does that mean he was only half as valuable and should be rated half as highly as Nick (assuming they performed equally during their minutes on the floor)? Minutes do matter and thus the NEP is the dominant factor in the NEP Rating. A player’s ability to contribute effectively even when playing fewer minutes or coming off the bench is also very important to rating that player. The difference in minutes becomes even more important when comparing players between different teams. Some teams play their starters 34+ minutes while others may play their starters significantly fewer than 30 minutes. Obviously the 30+ minute players run up more stats and hence a higher NEP. In order to make a valid comparison the n-NEP must be incorporated.
| | NEP/Pt | This is the NEP divided by points scored. A point scored is worth 1 NEP. Thus if a player did nothing but score his NEP would be equal to his Pts. Hence, the difference between NEP and Pts is the contribution that player made to the primary goals other than scoring. Last Monday Ricky Clemons of Missouri scored a team high 19 points but had an NEP in the 8 range. This would indicate his non-scoring stats actually lost his team 11 points. By taking the NEP/Pt ratio we can gauge the non-scoring aspects of a player's performance. An NEP/Pt above one indicates the player was a positive impact aside from his scoring.
| | SE | Shooting Efficiency - This is simply the points scored divided by the total number of possible points scored if every FGA and FTA had been made. This is sort of an attempt to merge FG%, 3FG% and FT% into one shooting stat.
| | M-SE | Modified Shooting Efficiency - This is the same as SE except it assumes all FGAs are 2FGAs. The reason for this is not to penalize the 3-point shooters. Theoretically you can have a 3FG% only 2/3rds as high as a corresponding 2FG% since 3-point shots are worth 50% more.
| | %OR | Percent of Offensive Rebounds - This is a team only stat. It is the percentage of rebounds a team gets at their offensive end of the total rebounds at their offensive end. The NCAA average is about 33%. If a team gets more than 33% of their offensive rebounds they are above average.
| | %DR | Percent of Defensive Rebounds - This is a team only stat. It is the percentage of rebounds a team gets at their defensive end of the total rebounds at their defensive end. The NCAA average is about 67%. If a team gets more than 67% of their offensive rebounds they are above average.
| | %TR | Percent of Total Rebounds - This is a team only stat. It is the percentage of total rebounds a team gets to all the rebounds in the game. The NCAA average is, of course, 50%.
| | %2FG | Percent of points scored from 2-pont field goal attempts - This stat is used to assess a team's strength and strategy. For instance, Texas Tech is last in the nation (all 327 Div I teams) in %3FG (discussed below). All other things being equal, this would indicate a potential defensive strategy would be to dig down in the post and double-team the post players or prevent the drive while allowing more open perimeter players.
| | %3FG | Percent of points scored from 3-pont field goal attempts - See discussion for %2FGA.
| | %FT | Percent of points scored from Free Throws - This is obviously an indication of how often a team gets to the FT line especially in proportion to their other scoring. There are many ramifications this has on strategy but the length of this column does not allow for a lengthy discussion at this time.
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As I mentioned above there are limitless possibilities for derivative stats. I’m sure many coaches have all sorts of sundry calculations they track for various reasons. This is just an example of those derivative stats I use on a regular basis.
CONCLUSION
So what? This is all very nice, but what the heck difference does any of it make? I have gone to all the discussion above of defining the various types of stats so we can now tie it all together and see if we can use these to glean any meaningful information. Going back to my initial discussion, what is my goal in tracking stats? I track stats for several reasons. First of all I have an engineering degree so I am genetically predisposed to numbers. I like the challenge of writing the programs and keeping my mind exercised by poring over all these numbers. That is just a personal thing. The reason I share these stats and analyses is because for someone who is so inclined these stats (primary, secondary, and derivative) can and do provide insight into the abilities, the performances, the strengths, the weaknesses, and the effectiveness of individual players and the strategy, tactics, strengths, weaknesses, and execution of teams.
What is the most important stat? IMHO, the most important and central stat (other than, of course, SM-scoring margin) is PPP (points per possession). The entire NEP is centered around this very important stat. If you want to know how well a team did on offense look at the offensive PPP, likewise on defense. The average PPP in the NCAA is 0.85. In other words, every time a team gains possession they will average scoring 0.85 points. To gauge a team's offensive execution look at the PPP. This single stat is the best indicator of overall offensive execution as well as overall defensive execution. It doesn’t answer the question “why” the execution was poor, but it gives a good measure of the execution. Below is a table of the PPP for every game this season.
We have had an offensive PPP below 0.85 five times this season, and we have lost all five games. We have allowed our opponents' PPP above 0.85 six times and lost four of those games. The PPP is a direct measure of the overall effectiveness of the teams' offensive and defensive execution.
After you look at the NEP the question now is “why” was there good, average, or poor execution? There are a myriad of stats that help us to evaluate that. The first order of business is to look for anomalous data. By this I mean any stat that deviates substantially from the average or norm. Be careful not to make too much out of any single stat. Here is a brief example of key stats and the averages or norms.
| A/FGM | KU averages about 0.60. If this stat gets too far below that it is an indication we did not move the ball well and resorted to a lot of one-on-one style play. Actually we have averaged only slightly lower in our 5 losses than in our 17 victories. Many teams have excellent win/loss records with very low A/FGM ratios (i.e. Cincinnati). A low A/FGM ratio for KU indicates we were not executing and more specifically, our perimeter players were not executing.
| | %LoB | This is a direct measure of how well we took care of the ball. Typically you want to get a shot attempt in at least 85% of your possessions which corresponds to a %LoB of 15%. A %LoB of greater than 18% starts to get into a danger area. We have averaged about 17% this season. Our worst game was against North Carolina where we turned the ball over on 26.8% of our possessions.
| | SE | This is a very significant stat. Obviously shooting well is a key to winning. The shooting efficiency is a close correlation to winning. In fact our 5 worst shooting games are the five games we lost. We can look at other stats to see why we shot poorly such as A/FGM, % of shot attempts that were 3 pointers, and percentage of shots from the perimeter players versus post players. KU averages about 55% in SE.
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The discussion is endless when you start trying to delve deeper and deeper into the stats. Your imagination is the only limit to what you can do. The key is to isolate each stat and try to determine what aspect of the game it measures or diagnoses.
You are probably pretty fed up with a discussion of stats by now so lets take a look at the Kansas State game.
Kansas vs. Kansas State
The Jayhawks have now beaten the Wildcats in Manhattan 20 straight years. That is simply amazing. Roy Williams has never lost in Manhattan and the Jayhawks have never lost in Bramlage Coliseum. In fact, Roy Williams is 35 and 4 against Kansas St. And considering he lost two of his first three against K-State in 1989, that is an amazing streak. This game was touch and go for 30+ minutes. Then a phone booth appeared and out popped superman, a.k.a. Kirk Hinrich. Kirk looked every bit the All American against Kansas St. He very purposefully took this team on his shoulders and pulled them to a decisive victory. His defense was superb as always, and his offense was outstanding down the stretch. When Collison had to leave the game with foul trouble I was a bit alarmed. Nick was having a pretty poor game but he is, after all, an All-American. K-State seemed to be playing over their heads and clearly wanted this game badly. It was a recipe for a KU disaster. That’s when Kirk stepped into the phone booth and emerged a superhero. He dished, he shot, he hustled and he shut down DeJesus and Hart. Miles was also very impressive down the stretch on the defensive end and sank some key shots. Langford continued to defy gravity and other natural laws in his play around the basket. He is turning into a human highlight reel. Graves had a solid game for the most part. We had good mental toughness and won a game in the stretch where we have faded at other times this year.
Player of the Game: Who else but Kirk (Superman) Hinrich. He scored 28 points, just one shy of his career high. He also dished out 5 assists and had 5 rebounds. He did have 3 TOs and missed 4 of 6 FTs, so even superman isn’t perfect. Most importantly, Kirk stepped up and led in All-American fashion. He made everyone on the court better. One play in particular sticks out in my mind. After a defensive rebound late in the game, Kirk gets the ball and hustles down court in that way he does. You know what I’m talking about. By the time he was to halfcourt he had passed 3 K-State players because they simply had run out of gas. It was sheer mental toughness to play through the fatigue. Kirk played a game high 39 minutes.
| NEP | n-NEP | NEP
Rating | NEP/Pt
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| Hinrich | 35.84 | 36.76 | 36.16 | 1.28
| | Miles | 26.03 | 30.62 | 27.64 | 2.00
| | Langford | 26.32 | 28.45 | 27.07 | 1.46
| | Lee | 7.83 | 26.10 | 10.57 | 2.61
| | Nash | 15.04 | 25.07 | 18.55 | 7.52
| | Graves | 15.51 | 22.98 | 18.12 | 1.94
| | Collison | 11.50 | 18.40 | 13.92 | 1.15
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Significant Stats of the Game: This is not really a significant stat, but it is certainly a rare one: Nick Collison had the lowest player rating of all Jayhawks in the game. That will likely never happen again. Nick had a bad game. It’s just that simple. Every other Jayhawk had n-NEPs above 22, which is excellent. Every perimeter player had an n-NEP above 25, which is amazing. Bryant Nash may have played his best game as a Jayhawk.
In the last game against K-State we tied in rebounding. In Manhattan we outrebounded them by 12 and dominated our defensive glass garnering 75% of the defensive rebounds. Not allowing KSU many second chances really kept us in the game early on. KU was just about on their average PPP of 0.976, and our defense was just a little below average allowing K-State a PPP of 0.800. A huge stat is the difference in 3 pointers by K-State in the 1st and 2nd halves. They hit 7 of 13 in the first half and scored about 60% of their points from the arc. Obviously if we could shut them down on the perimeter they would have to pick up the inside play. We made some adjustments at the half and allowed them only 1 second half trey out of 5 attempts. Considering they were behind that is really a great feat since you would expect them to start bombing treys to catch up. They were a one-trick pony (shooting treys) which allowed then to stay in the game. Once we took that trick away they faded. It appeared the Wildcats ran out of gas in the last 6-7 minutes. What is encouraging about that is the Jayhawks had logged almost 20 more minutes than the Wildcats at that time. That would lead you to believe the Jayhawks really are a conditioned and mentally tough team. Let us not forget the value of a good coaching staff that can not only make the strategic adjustments when called for but can properly motivate the team to execute regardless of their fatigue. The first 33 minutes of this game provided some fodder for Jayhawk critics but the last 7 minutes more than erased any doubts.
Aaron has been criticized of late, especially after his less than adequate performance against Missouri. The wolves came out when Roy showed his own disappointment with Aaron. The bible tells us that he who spares the rod spoils the child. I don’t believe for a minute that any public statements made by Roy are for any purpose other than to ultimately teach and motivate his players. Please do not forget that the basis of Roy’s relationship with his players is TRUST and LOVE. When he has credibly established these, he has earned the right to criticize them (for their own good). Roy has never shown a proclivity to be mean-spirited in any way shape or form. In fact the opposite is true. His entire career has been a testimony to his “players first” nature. I find it almost amusing how some critics will take every opportunity to jump on the slightest overt indication of disharmony. Roy has inculcated a strong desire in his players to please him and the coaching staff. The players know that any criticism they receive from Roy, publicly or privately, is based in his deep and sincere desire to act in the players best interest.
So was Aaron deserving of the criticism? To understand the answer you have to look at the bigger picture. Look at the table below and tell me if Aaron deserves criticism this year. This is the year-to-date NEP numbers.
| NEP | n-NEP | NEP
Rating | A/TO | SE | Min/G | RPG | PPG | APG
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| Collison | 660.6 | 38.69 | 33.06 | 0.87 | 58.97 | 31.05 | 8.7 | 18.8 | 2.18
| | Simien | 298.6 | 36.52 | 28.95 | 0.41 | 64.75 | 27.25 | 8.7 | 15.9 | 0.58
| | Hinrich | 563.5 | 32.53 | 28.83 | 2.00 | 51.92 | 33.00 | 3.6 | 18.0 | 3.62
| | Miles | 511.4 | 30.12 | 25.65 | 2.11 | 41.70 | 30.86 | 3.6 | 8.6 | 7.27
| | Langford | 497.2 | 28.05 | 24.51 | 1.22 | 53.06 | 32.23 | 5.1 | 16.1 | 2.05
| | Graves | 263.5 | 27.30 | 17.34 | 0.40 | 49.36 | 17.55 | 5.8 | 5.2 | 0.64
| | Lee | 195.8 | 25.77 | 15.08 | 1.44 | 52.54 | 14.48 | 2.4 | 4.4 | 1.24
| | Moody | 22.2 | 26.07 | 10.15 | 1.00 | 38.89 | 2.43 | 0.4 | 0.5 | 0.21
| | Nash | 114.0 | 18.76 | 9.93 | 0.48 | 40.12 | 11.05 | 2.1 | 3.0 | 0.45
| | Hawkins | 62.4 | 20.12 | 9.17 | 1.22 | 31.03 | 6.53 | 0.9 | 1.4 | 0.58
| | Vinson | 16.1 | 18.36 | 7.17 | 0.67 | 24.49 | 2.50 | 0.4 | 0.9 | 0.14
| | Niang | 53.0 | 14.32 | 7.04 | 0.00 | 31.88 | 8.71 | 1.7 | 1.3 | 0.00
| | Olson | 3.7 | 4.47 | 1.74 | 0.00 | 18.18 | 2.36 | 0.3 | 0.3 | 0.00
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Aaron has a respectable NEP Rating and actually ranks ahead of Langford. He is rebounding as well as Kirk and has the best A/TO ratio on the team. He leads the team in assists and is third in total NEP. So what’s not to like? Well I guess a 41.7% shooting efficiency is not to like. But is this why Roy was upset with Aaron. I think to understand that you have to look at the following table. This is the data for Aaron for each game this year.
As the point guard and leader, Aaron should have a NEP above 25 every game. His n-NEP especially should never dip below 25. If you peruse these numbers you see some outstanding games. His two game stretch against Texas and Nebraska was just spectacular. It was preceded by three games where Aaron was sort of AWOL and played below par. But the real culprit of Roy’s ire can be seen in the Oregon, North Carolina and Missouri games. Aaron has occasionally this season just totally lost his composure. Roy does not abide that from any player very well, but he will be especially tough on his point guard. Aaron is having an outstanding year by any measure. He will finish his career as one of the all-time KU great PGs. But to be the PG we need and Roy wants, Aaron needs to eliminate the occasional brain-dead game. I personally love Aaron’s play, but I share Roy’s criticism of Aaron’s volatility. But any critical spirit I possess for Aaron is wrapped in a hope and desire to see him be the best he can be.
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