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Predicting Player Performance

This summer is the 4th season that I am predicting next year's standings based on the tempo free player statistics. Last year my preseason predictions model appeared in ESPN the magazine, and my most recent 2013 predictions were featured on CBSSports.com. This summer I hope to make my predictions methodology more transparent to the reader, and as always I am looking for ways to improve the model. Let’s start with a fact that I mention rather frequently:

Class

Avg ORtg

Fr.

92.7

So.

97.3

Jr.

99.5

Sr.

102.1

(Data are from 2003-2012 seasons, all D1 teams)

On average, the biggest improvement in efficiency happens between a player’s freshman year and sophomore year. Of course just looking at the raw numbers can be a bit misleading because of players transferring out. Typically extremely inefficient players either transfer to smaller programs or stop playing D1 basketball completely.  But if we limit the data to 4-year players, the pattern is still there:

Class

Avg ORtg

Fr.

95.2

So.

100.4

Jr.

103.1

Sr.

104.5

Returning freshmen typically show the greatest improvement. Keep in mind that this is the average and that individual development patterns can vary widely. I will try discuss the unpredictability of player development in a future column, but the main point I want to emphasize is that this is not simply a case where everyone gets a little better every year. What happens is some players show dramatic improvement, some players tread water, and a few players get worse.

Not surprisingly, this also shows up in the team data. Teams that return more freshmen minutes are more likely to improve the following season. To my knowledge, my preseason predictions model is the only model that incorporates the importance of freshmen development.

In many ways, freshmen development is the kind of fact that is difficult to wrap our heads around when writing preseason predictions. When we predict the standings in a conference, we like known commodities. We like to write about can’t miss recruits and established players. We don’t like to talk about probabilities. West Virginia returns four freshmen who saw significant playing time last season (Jabari Hinds, Gary Browne, Aaron Brown, and Keaton Miles). None of those players was particularly dominant and that means that a lot of people are going to write West Virginia off next season. But the probability is that at least a couple of those players will be substantially better. I just can’t tell you who.

Different Development Curves?

One of the things I have been thinking about lately is whether we need to think more about the development curves for different types of players. Last summer Drew Cannon wrote about how big men develop more slowly than guards.

But last week when I was writing a look-back column on Frank Martin, I was thinking how we should really break out other types of players too. In particular, three point shooters are significantly more likely to be efficient off-the-bat, and significantly more likely to be efficient throughout their careers.  Conversely point-guards without an outside shot are typically terrible as freshman, and while they improve they rarely match the efficiency of other players:

Class

Avg ORtg - All Four Year Players

Avg ORtg - Three Pt Shooters

Avg ORtg - Non-Shooting PGs

Fr.

95.2

97.5

88.9

So.

100.4

102.9

95.4

Jr.

103.1

105.8

98.6

Sr.

104.5

107.1

101.0

I define a player as a Three Pt Shooter if he takes more than 4 threes per 40 minutes played in his career. The definition is based on attempts, not makes. I define a player as a Non-Shooting PG if he earns at least 4 assists per 40 minutes played in his career. This assist cutoff was chosen to be restrictive enough to exclude players like Henry Sims, but that also means it excludes some guards who are typically viewed as point guards. I also broke the data down to look at combo guards, guards who have an outside shot and set up their teammates. It turns out that passers who have three-point range tend to have similar efficiency to spot up three point shooters and thus I grouped all three point shooters together in the table.

This table is one of the key reasons people have criticized ORtg. Players that are spot-up three point shooters tend to be more efficient even though they might not be the most valuable players on the floor. Some people have argued that Dean Oliver’s formula should be adjusted to give the other teammates credit for getting open three point looks. Most analysts handle this by concluding that ORtg is a stat that requires context (such as Usage rates) in order to interpret it. But you can’t argue with the connection between three point shooting and team efficiency. Teams as varied as Florida and Wisconsin have proven that by taking and making a bunch of threes you can have an efficient offense. And freshmen who shoot threes are usually the most efficient incoming players.

Forwards

Over half of the D1 players with multiple seasons can be described as passers or three point shooters. The rest are typically described as “forwards”. For this group you can typically define efficiency based on whether the players grab offensive rebounds or not:

Class

Avg ORtg - All Forwards

Avg ORtg - Offensive Rebounders

Avg ORtg - Few Offensive Rebounds

Fr.

94.1

99.2

93.5

So.

98.9

104.0

98.2

Jr.

101.3

106.6

100.6

Sr.

102.7

107.8

102.0

I define a forward as an Offensive Rebounder if he grabs at least 4 offensive rebounds per 40 minutes played, a skill that about 15% of forwards have.

If you want to be an elite offensive player at the forward position, offensive rebounding is a skill you almost certainly must possess. I am a little concerned with defining players based on this characteristic, because coaching can have such a big impact on this skill. In particular, a big reason to be optimistic about South Carolina next year is that Frank Martin will get his forwards to grab offensive boards. But whatever the source of the skill, offensive rebounding is essential to efficient play at the forward position.

Overall, when I look at these various tables, I think the most interesting thing is that the development curves are fairly similar. Pure point-guards tend to improve more between their freshmen and sophomore seasons, but for the most part players with various skill profiles tend to develop at a very similar rate.

But what these data really suggest is that we shouldn’t have equivalent expectations for players at every position. Offensive rebounders and three point shooters might be good right off the bat. But small forwards and point-guards are rarely polished players until later in their career.

Have you reached your potential?

When thinking about player development, the next table brings up even more questions. The X-axis shows a player’s ORtg as a Junior and the Y-axis shows the change in ORtg between the Junior and Senior season.

The table essentially shows two things. First, there is a negative correlation. Players with low efficiency ratings are much more likely to show a dramatic improvement. Second, the upper right hand corner of the picture is almost empty. If you have an ORtg of 120 and come back for your senior season, you are very unlikely to be more efficient. Now there are a lot of explanations, from defenses adjusting, to players changing their usage rates, but the main point seems true: When you have a very high ORtg, it is almost impossible to come back and post a better number.

But that makes me wonder whether the very first table in this column is really measuring a freshmen effect or simply a “room-for-growth” effect.  Should I expect Jabari Hinds, Gary Browne, Aaron Brown, and Keaton Miles to take a big step forward for West Virginia because they are freshmen or because they made a lot of mistakes last year and have plenty of room to grow?

Perhaps it doesn’t matter for those players, but the real question is whether we should really expect a dominant freshman to get much better. Luke Winn recently listed the most dominant returning sophomores, and I’ll use Cody Zeller as an example.  Zeller had an incredibly efficient freshman campaign posting an ORtg near 127. Is there any hope of him improving on that number? Based on my analysis of the numbers, returning elite players are more likely plateau, even if they were freshmen. Much like Jared Sullinger, I expect Zeller to have a great sophomore season. But I don’t expect him to have a more efficient second season.

The biggest challenge is that I simply don’t know whether Zeller has reached his potential. And that might be a question only scouts can truly answer. And given the large number of scouts who are evaluating players for both the NBA and coming out of high school, there must be some information about whether players have additional room to grow.

As Rob Dauster emphasized recently, and as I noted in a podcast with Matt Norlander earlier this year, the high school recruiting data implicitly includes both a current ability ranking and a potential ranking. But my challenge to all the fantastic scouts working extremely hard to bring us all that recruiting data is to provide us a numeric rank of both. Show us a current ability ranking and a separate potential ranking.

I want a metric that says that Minnesota’s Rodney Williams had an extremely high ceiling, but was not at that level when he enrolled in college. I want a metric that says that Nerlens Noel is a great shot-blocker, but may lack the ceiling of Anthony Davis.

Meanwhile, my challenge to the Rob Dauster’s of the world who are thinking about synthesizing recruit information is this: Can you synthesize those “scouting comments” on Rivals.com and other websites into something more? Is there a consensus on who still lacks an outside shot, who has the most leaping ability, and who has the highest upside? Even if there aren’t great metrics available, there are often very revealing comments about some of these additional factors. And pulling together a consensus ranking of those other comments could be extremely valuable.

Predicting college performance is never going to be an exact science. Young players are at a developmental stage where almost anything can happen. But there is some incredible scouting data being created. And finding a better way to use that data will help us to make more informed predictions in the future.

Have you been thinking about player development or freshmen performance? Contact me on Twitter @DanHanner or email me DLHanner@gmail.com.
 

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