FanPost

How Well Does PTL Performance Predict Season Performance?

USA TODAY Sports

This is really good stuff -- well worth reading. -- ross

Hi All,

I am a frequent reader of BHGP, but I don't post here very often. Recently, though, I have found some down time after completing two of my three classes this summer, so I thought I would attempt to make a contribution. Some of you may remember me from Black and Gold Box Score or more likely from The High Porch Picnic. But, if you have no idea who I am, well, you should be warned I tend to be a little long-winded and there will be a little bit of math involved in what follows...

So, like many Hawkeye fans nowadays, I'm finding my enthusiasm lately to be a little higher for the men's basketball team than I am for their college football counterpart. Not to say that I'm not looking forward to the upcoming football season, because who isn't? It's just that Fran has quite a collection of talent returning, and, as a result, I have found myself taking a heavier interest in the Prime Time League results this year. Namely, the statistics.

A couple of weeks ago, Senor Jacobi sparked my interest with this quote:

Okay, there's not a lot we can figure out from PTL box scores (if anyone ever bothered to put them online anyway). So Steve Generic put up 31 points? Okay, cool. 29 points and 15 boards for Sophomore Forward? Neat, but that doesn't really tell us a whole lot about what to expect from these guys once the actual season starts.

Or does it?

Every year we walk a fine line between acknowledging the lack of defense played in North Liberty, while also celebrating the offensive performances. This is for good reason. You see, we know that it is probably better to see a player put up a huge point total in a league that plays little defense, than put up a small total of points in a league that plays little defense. But, how well does this preseason offensive performance predict offensive performance in the upcoming season? Thanks to Mr. Jacobi, I decided to ask just that question.

Unfortunately, in performing this exercise, we are stuck with a small sample size. There is no way around it. Based on my ability to Google, I was only able to find PTL stats back to the Summer of 2010, or McCaffery's first season as head coach. (If anybody has stats from farther back, please let me know.) This only gives us three years of results to use because the 2013-2014 season has yet to be played. So, next summer I will have four years of data for this population. Anyway, thanks to Brendan Stiles at HawkeyeDrive.com I was able to get stats for Iowa's players in 2010, 2011, and 2012. What I was most concerned with was offensive performance, i.e. scoring. Thus, I took average points per game scored by a player in the PTL and compared it to what that player averaged in the upcoming season, in order to see how well the former predicted that latter. Let's take a look at the chart:

That's a pretty nice little correlation, now isn't it? Yes, small sample size, but it matches pretty well with what I think fan perceptions are. Many of the guys who score a lot of points in the PTL tend to score a lot of points when the regular season tips off. The r^2 is about 0.59, which means average points per game scored in the PTL can predict about 59% of a player's average points per game scored in season. Of course, points per game is a flawed statistic, as it is reliant upon minutes played. Ideally, I would prefer to use something like points per minute, but I have yet to come across any place that has total minutes played in the PTL. (Again, if any of you are aware of such a magical place, let me know.) In other words, this is as good as we are going to get. Now, let's talk about what this may or may not tell us.

First, here is the list of all eligible players:

Player PTL Season xSeason Difference
2011-2012 Gatens 21.3 15.2 8.3 6.9
2012-2013 Marble 21 15 8.1 6.9
2011-2012 White 19 11.1 7.2 3.9
2011-2012 Oglesby 13.3 6.4 4.4 2.0
2012-2013 White 27.3 12.8 11.2 1.6
2012-2013 Woodbury 11.3 4.9 3.4 1.5
2010-2011 Gatens 27.3 12.6 11.2 1.4
2011-2012 McCabe 19 7.8 7.2 0.6
2011-2012 Marble 27 11.5 11.0 0.5
2012-2013 Gesell 21.7 8.7 8.5 0.2
2010-2011 Archie 7.4 1.7 1.5 0.2
2011-2012 Olaseni 7.1 1.4 1.4 0.0
2011-2012 Archie 6.9 1.3 1.3 0.0
2012-2013 Clemmons 13.3 4.3 4.4 -0.1
2010-2011 May 21.8 7.8 8.5 -0.7
2010-2011 McCabe 17.9 5.8 6.6 -0.8
2012-2013 Oglesby 15.4 4.5 5.4 -0.9
2010-2011 Marble 18.3 5.7 6.8 -1.1
2010-2011 Brommer 13.4 3.1 4.4 -1.3
2011-2012 Brommer 10 1.4 2.8 -1.4
2011-2012 Cartwright 19.6 6 7.5 -1.5
2012-2013 McCabe 19.2 5.7 7.3 -1.6
2011-2012 Basabe 24.7 8.2 9.9 -1.7
2010-2011 Basabe 30.6 11 12.8 -1.8
2012-2013 May 18.8 5.2 7.1 -1.9
2012-2013 Basabe 24.1 6.8 9.6 -2.8
2012-2013 Olaseni 16.5 2.7 5.9 -3.2
2011-2013 May 23 4.3 9.1 -4.8

The table shows the players who outscored their predicted points per game average based on their PTL performance. "xSeason" just means the expected points per game based on PTL performance.

Top Overperformers

1. and 2. 2011-2012 Matt Gatens (xSeason- 8.3 PPG, Season- 15.2 PPG, Difference- 6.9 PPG) and 2012-2013 Devyn Marble (xSeason- 8.1 PPG, Season- 15 PPG, Difference- 6.9 PPG)

Data points which fall above the trend line are the players who had mediocre scoring averages in the PTL and went on to produce excellent scoring seasons for Iowa. The two big outliers here are Matt Gatens' senior year and Devyn Marble last year. In the Summer before Gatens' senior year he put up 21.3 points per game in the PTL before going on to put up 15.2 in the regular season. Marble, meanwhile, had similar averages of 21 in the PTL and 15 when it really mattered. Oddly enough, both players averaged about 27 points per game in the PTL the year before. Both just did not shoot as well as they had the previous Summer. The main point here is, the trend line would have expected both players to average about 8 points per game in the regular season, yet they averaged 15.

3. 2011-2012 Aaron White (xSeason- 7.2 PPG, Season- 11.1 PPG, Difference- 3.9 PPG)

Aaron White's freshman year was a pleasant surprise. White had a pretty mediocre (by PTL standards) offensive performance during his first Summer on campus, averaging 19 points per game. However, little did we know, that the double-double he put up in his first game against Chicago State was just the beginning of the good times that lie ahead.

Top Underperformers

1. 2011-2012 Eric May (xSeason- 9.1 PPG, Season- 4.3 PPG, Difference- -4.8 PPG)

I guess the data felt the same way about Eric May's Junior season as we did, as it was the most disappointing regular season performance based off of PTL performance. You all remember that year. You know, the year where we all thought Eric May was broken. The trend line would have predicted him to average about 9 points per game based upon his 23 PTL points per game, but reality was only 4.3.

2. 2012-2013 Gabe Olaseni (xSeason- 5.9 PPG, Season- 2.7 PPG, Difference- -3.2 PPG)

Number two would have been 2011-2012 Cully Payne, but he only played five games due to injury, so I am not including him in our sample. With Olaseni, this is where playing time comes into play, as Gabe only averaged 10.7 minutes per game last year. Olaseni showed improvement in his second PTL stint, improving from 7.1 PPG as a freshman to 16.5 PPG as a Sophomore. Of course, 16.5 PPG by PTL standards means you most likely still need to work on your offensive game, and competing with White, Basabe, McCabe, and Woodbury for playing time was going to make it tough to get playing time, and by extension, points in the regular season.

3. 2012-2013 Melsahn Basabe (xSeason- 9.6 PPG, Season- 6.8 PPG, Difference- -2.8 PPG)

Yeah, this isn't all that different from Olaseni's predicament. Basabe was more offensively advanced than Olaseni was, but Iowa finally had some decent depth down low and Basabe saw his playing time decrease, as a result.

Other Observations

  • The trend line almost exactly predicted Devyn Marble's coming out season. During Marble's sophomore season, he solidified himself as one of the star players on the team. (We could debate all day who is the star on the team, him or White, all day.) He went from averaging 5.7 points per game in his rookie year to 11.5 points per game in that breakout year. And his performance from the PTL would have you believe it was pretty obvious that it was coming. The trend line predicted him to score 11 points per game, to which he responded with 11.5.
  • The trend line does a pretty decent job predicting Basabe's offensive drop off. Remember when Basabe looked like an offensive star during his freshman year? 30 points per game in the PTL led to 11 points per game come real time. Sophomore year it went to 24.7 and 8.2, and then Junior year it went to 24.1 and 6.8. Of course, the trend line has no way if knowing that Iowa would be more loaded with actual talented basketball players in the frontcourt for Basabe's Junior season, which explains why it expected him to averaged about 2.5 more points per game than he did last season.
  • I averaged the points per game averaged by every Iowa player who participated in the PTL since Fran has been here and this year's crop of players is the highest scoring bunch. By far. This year's group averaged 23.5 points per game, while the next highest were the 2010 PTL players, who averaged 19.6. Meanwhile, 2012's players averaged 18.9 and 2011's players averaged a meager 17.1. What does this mean? Well, it could mean a couple of things. First and most obviously, Fran has accumulated more talent this year than Iowa has had in recent memory. Especially as far as this data is concerned. So, it is no surprise that this year's bunch of guys were better scorers. Another thing this may possibly signal, is an increased tempo, as a result of Fran's style of play and recruiting. Eric May was the last guy to actually play under Lickliter, and even though Marble and McCabe were recruited by Lick, most of this roster fits the long and athletic mold that Fran looks for in a basketball player. I could possibly compare the average scoring of each PTL year to see if this may be the case, but I'm too lazy at this point. So, this is just a guess. But, make note that this year's crop of players were not like players of the other years, for whatever reason.

What Might this Mean for Next Season?

So, now that we've looked at the past, let's take a look at what the formula expects for next season. For these numbers, I again used Hawkeye Drive's end-of-PTL recap.

PTL xSeason
2013-2014 Jok 29.5 12.3
2013-2014White 29.0 12.0
2013-2014Marble 27.4 11.2
2013-2014Gesell 26.3 10.7
2013-2014Olaseni 25.3 10.2
2013-2014 Clemmons 25.1 10.1
2013-2014 Woodbury 23.0 9.1
2013-2014 McCabe 22.7 9.0
2013-2014 Uthoff 21.9 8.6
2013-2014 Oglesby 17.0 6.2
2013-2014Meyer 11.1 3.3

More Bullet Point Observations

  • Well, hello there, Peter Jok. But, let's hold on just a minute now. As we saw with Olaseni and Basabe, this model isn't very good at understanding team depth. Jok is going to play this year, and he will probably play some pretty decent minutes. However, he seems to be the heir to Marble's throne on the wing, and Iowa has Gesell, Clemmons, Oglesby, White, and Uthoff who can all play variations of the 2 or the 3 depending on the lineup. Fran has a lot of options for lineups this year, and this is surely going to cut into Jok's playing time and scoring. Also, just because Jok put up 29 points a game in the PTL this year, it is not necessarily a guarantee of future offensive prowess. Basabe averaged 30 during his freshman year and has dropped every year since. I do think Jok's case is different, as he seems to be a great shooter and athlete, and he plays a position where he is likely to have the ball in his hands more often. Plus, before he dealt with injuries as a high schooler, he was considered to be one of the top recruits in the nation. So, the pedigree is there, and I think Jok has a bright future taking over for Marble. Until then, though, I am predicting that Jok will have the biggest gap between PTL performance and season performance when I run these numbers again next year.
  • You can't really argue with White (even based on only 1 game of PTL action) or Marble's predictions. Iowa will have more scoring options next year, so I think it will be hard for Marble to put up 15 per game again.
  • Unfortunately, Basabe did not take part in this Summer's PTL, so he won't be in next year's batch of numbers. /has a sad
  • These projections, as a whole, are way too optimistic. Using logic, there are probably going to be more guys who are going to be under their predictions after next year. Again, this is due to the fact that Iowa seems to be the deepest they have been in years. This year's crop of PTL players is different than past crops.There is just no way that six players are going to put up double-digit points per game this year. If that was the case, look out, rest of the country. What I would expect to be more likely, would be for White and Marble to be the top scorers at around 12. I think Gesell can put up about 9-10 per game, while Uthoff can come in around 7. I think Basabe and Woodbury will be around 6. Clemmons, McCabe, and Jok could be around 5, with Olaseni at 4, Oglesby around 3 and Meyer at 1. That puts the team at around 75-76 points per game. Considering they averaged 70.1 last year and they play in the Big Ten, a conference that isn't really known for getting up and down the court, this could be a little high. They should score more than last year, but somewhere around 73-74 might be a little more realistic.

So, that's that. I hope this was somewhat enlightening. Looking at the data, despite the small sample size, I believe there is some things that we can take away from this exercise. Mainly, PTL performance does seem to be a decent predictor of how good a player is going to be offensively. At least it has been in the past. It definitely has it's limitations (taking into account depth), but hopefully it will get better with the more data that we get. It will be interesting to see the data after this upcoming season, considering how different this year's players were. If Fran continues to build teams with depth like this year's, there is a good chance that in a handful of years, the model will have adjusted to the new norm. It took a while for the basketball program to recover from the nuclear holocaust that were the Lickliter years. Fortunately, it looks like Fran may be starting to build something special.

Unless otherwise expressly indicated by BHGP editors, this FanPost is strictly the viewpoint of the author and is not endorsed by BHGP in any way.

X
Log In Sign Up

forgot?
Log In Sign Up

Please choose a new SB Nation username and password

As part of the new SB Nation launch, prior users will need to choose a permanent username, along with a new password.

Your username will be used to login to SB Nation going forward.

I already have a Vox Media account!

Verify Vox Media account

Please login to your Vox Media account. This account will be linked to your previously existing Eater account.

Please choose a new SB Nation username and password

As part of the new SB Nation launch, prior MT authors will need to choose a new username and password.

Your username will be used to login to SB Nation going forward.

Forgot password?

We'll email you a reset link.

If you signed up using a 3rd party account like Facebook or Twitter, please login with it instead.

Forgot password?

Try another email?

Almost done,

By becoming a registered user, you are also agreeing to our Terms and confirming that you have read our Privacy Policy.

Join Black Heart Gold Pants

You must be a member of Black Heart Gold Pants to participate.

We have our own Community Guidelines at Black Heart Gold Pants. You should read them.

Join Black Heart Gold Pants

You must be a member of Black Heart Gold Pants to participate.

We have our own Community Guidelines at Black Heart Gold Pants. You should read them.

Spinner.vc97ec6e

Authenticating

Great!

Choose an available username to complete sign up.

In order to provide our users with a better overall experience, we ask for more information from Facebook when using it to login so that we can learn more about our audience and provide you with the best possible experience. We do not store specific user data and the sharing of it is not required to login with Facebook.

tracking_pixel_9347_tracker