This is my first report using the new Player Plus Minus System. The results seem to be more consistent with most people’s opinions of the play of the players involved, so I think this passes the sniff test.

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Cornley now moves to the top of the plus/minus list with 10 positive games and only two negative ones. The top five in order are: Cornley, Battle, Claxton, Walker, and Morrissey. Battle has been steadily progressing his way up the plus/minus charts and it shows here.

Here’s a quick snapshot of the meaning of the terms in this Plus Minus System chart.

PM Rating is the sum total of the individual game minus scores. The game plus minus score is the difference between the delta scoring of the teams while the player was on the floor and the delta scoring while he was off.

G+ is the number of games where a player had a positive plus/minus game score.

G- is the number of games here a player had a negative plus/minus game score.

G% is the percent of all games played that were positive plus/minus games.

PM Rating per game is the PM rating divided by the number of games that the player participated in.

PM

Note: This is an interactive chart and these columns are sortable simply by clicking on the column header.

Click on the chart to analyze the details.

The individual leaders are:

Percent Minutes – Geary Claxton 76.1%
Offensive Rating – Brandon Hassell 120
Percent Possessions – Geary Claxton 30.8%
Percent Shots – Geary Claxton 30.3%
Effective FG% – Danny Morrissey 61.3%
Offensive RB% – Brandon Hassell 18.9%
Defensive RB% – Geary Claxton 20.8%
Free Throw Rate – David Jackson 89.1%
Assist Rate – Talor Battle 23.6%
Turnover Rate – Mike Walker 3.2%
Percent Blocks – Brandon Hassell 4.2%
Percent Steals – Stanley Pringle 4.4%
Free Throw Percent – Stanley Pringle 100%
2PT Field Goal% – Danny Morrissey 81.3%
3PT Field Goal% – Jamelle Cornley 50.0%
Some of these numbers particularly stand out.

• Claxton’s involvement in 30.8% of possessions while he’s on the floor puts him way ahead of Jamelle Cornley’s second place 22.3 and ranks him in the top 35 nationally.
• Claxton is also taking 30.3% of the shots while he is on the floor which is well ahead of Mike Walker’s surprising second place rate of 22.7%
• Hassell (18.9) and Jones (16.2) are substantially ahead of the others in their offensive rebounding
• On the other end of the court, Claxton controls the defensive boards at a 20.8 clip.
• DJ Jackson’s free throw rate (89.1), which measures how well you get to the line and make your free throws, is twice that of second place Andrew Jones (45.5).
• Mike Walker has an unbelievable turnover rate of only 3.2%. That’s leads the country. Brandon Hassel also has an exceptional number at 4.8%.
• Stanley Pringle’s 4.4 steal percentage is half again as much as any else on the team and places him in the top 100 (70 specifically) nationally.
• Pringle also has yet to miss a free throw, which puts him well ahead of 2nd place Andrew Jones at 73.3. Penn State’s notoriously poor free throw shooting doesn’t fall on the shoulders of their big men, the big guys are shooting exceptionally well. Jones (73.3), Hassell (72.7), Jackson (70.7), 2nd, 3rd, and 4th on the team, are more than doing their share.
• Morrisey has an incredible 2PT shooting percentage of 81.3. Evidently, Danny either takes threes or layups.
• Cornley leads in 3PT shooting at 50% but he’s only taken 4 shots. Pringle and Morrissey are 2-3 but they are far back at .375 and .373. I’d be content if Stanley’s numbers stay there but Danny needs to get his number back above 40%.

Are assists included anywhere? It seems the ratings will unfairly have a bias towards forwards/centers since they are less prone to turnovers and have more opportunities for rebounds than guards.

They are included in the scoring section. An assist is equivalent to .7 of a point. So in the case of Drew Neitzel his per game scoring average of 14.3, which ranks 8th, gets a bump of .7 x 4.7 to 17.6, or 5th overall.

Thus the guards have more of an opportunity to uplift their scoring than the guys in the front court do.

Also if the methodology favored the forwards/centers then we would expect a disproportionate number of them to rise to the top.

If I breakdown the 30 guys I analyzed, they fall into the following four buckets.

Guards = 16 or 53%
Wings = 7 or 23%
Forwards = 5 or 17%
Centers = 2 or 7%

When I look at the All Around top ten list and break them down, I get:

Guards = 6 or 60%
Wings = 2 or 20%
Forwards = 2 or 20%
Centers = 0 or 0%

Certainly, that’s no indicator of any position prejudice. If one of those guards had been a center, it would have been smack on the expected percentage – and given that the 10th guy was a guard and both the #11 and #12 are centers, that almost happened. So I’d say the position breakdown supports that the system doesn’t favor any one position over another.

Now what it does show is that guards are getting a lot of playing time, since there would appear to be a disproportionate number of them in the top 30 in mpg. The fair question might be why? First guess would be that a lot of teams might be utilizing a lot of three guard offense. Whether that’s true or not I will have to leave up to the guys who watch every game.

Raymar Morgan.

Say WHAT?

Using my modified Dean Oliver Four Factors methodology (for details on the method see the preceding article in this series, i.e. the Pete Maravich effect), I concluded exactly that.

First, lets check out the laundry list of candidates. The criteria to get on the watch list was to be among the top 30 in the league in minutes per game. Those 30 guys are (listed by mpg).

Using this list I ranked each of the 30 guys based on the scoring production (points and assists), turnovers (steals, blocks, turnovers, and fouls), rebounding (offensive and defensive), and free throws (free throws per field goal attempt). Here are the top ten from the list in each category.

For the All Around ranking, I weight each of the above categories (I’m only listing the top ten in each category but when I do the weighting and ranking I am using all 30 players). The top ten from that list are:

I’m sure the name at the top of the list is going to raise a few eyebrows. But, I’ve said many times, the purpose of all this number crunching isn’t to just reaffirm what conventional wisdom says. It’s to open the mind up to additional possibilities, it’s to make you think. And I would hope that any list with Raymar Morgan’s name at the top would get you thinking.

Sniff Test

The quickest and easiest way to evaluate any proposed new methodology is to ask “Does it pass the “Sniff Test”"? When your first reaction here is “something smells”, you should dig deeper into the data.

With the way that Eric Gordon topped each of the other lists, the initial reaction here would have to be “something seems a little fishy”.  However, when you look past Raymar Morgan, the typical names are there. #2 DJ White and #3 Geary Claxton are on everyone’s list of the top players in the league. I doubt that many would argue against Drew Neitzel being one of the top players in the league. Craig Moore placed #5 on each of the other three evaluations (NBA efficiency, Game Score, and Win Score). And Raymar Morgan, himself , was 2,3, and 4 in the other three rankings. So the basic methodology doesn’t appear to have a fundamental flaw.

So why is Eric Gordon so low? Perhaps because, as proficient a scorer as he is, he just isn’t solid enough in the other parts of his game to claim the best overall player crown. He’s clearly the best scorer in the league, by far, – and that huge scoring edge is what carries him to the top in the NBA, Game Score, and Win Score ratings. Gordon is also the best in the league at getting to the free throw line and making his shots, so where does he fall down?

The problem comes from the other two factors. Gordon doesn’t rebound well (25th) plus he’s significantly minus in the turnover department (24th). Interestingly, several of the other top candidates have flaws too. Morgan is below average in the turnover rankings (18th) but rebounds well (5th). Claxton, similarly has a turnover problem (27th) but also rebounds well (4th). Moore and Neitzell are the reverse. They protect the ball well (2nd and 8th) but don’t rebound all that well (17th and 28th). But Gordon is the one that comes up short in both.

That does raise another question? Is there anyone who does all four well. The answer to that is yes. DJ White really has no deficiencies in his game. He’s 10th in point production, 11th in turnovers, 3rd in rebounding, and 4th in free throws. While no one on the list is in the top ten in all four categories, DJ White comes the closest. Unfortunately, the two categories that he’s the lowest in, i.e. point production and turnovers, happen to be the two that carry the most overall weight (about 70% combined) in the equation so he comes up just short of Morgan.

So for the time being at least, Raymar Morgan gets to hold the crown of Best All Around player in the league. My gut feel, though, is that DJ White is going to lay claim to it during conference play. We shall see.

I’ll continue to update all on any changes to the rankings at various times during the season. Nobody is a runaway leader in this system and the rankings could change several times during conference play.

There’s also no doubt that Eric Gordon is an incredible talent. He topped all others using the three proficiency models that I used in my analysis of the Big Ten’s top players. But his scoring proficiency is so much greater than anyone else in the conference (he leads second place Geary Claxton by almost six points per game) that it’s quite possible there might be a little of the Pete Maravich effect, albeit at a much smaller level, at work here.

That thought led me to see if I could come up with an alternate way of determining the top guys. I chose to base my approach on Dean Oliver’s Four Factors. Basically, Oliver says the four keys to winning basketball are scoring, turnovers, rebounding, and free throw shooting. My approach was to analyze the Big Ten’s top players using those four factors.

For scoring, I looked at the points that a player scored plus how he assists of other players. However, I didn’t simply add assists to points. In the Wages of Wins, the authors determined that an assist was worth approximates .7 of a point, so I used that as my adjustment factor for adding assists to a player point totals.

I followed a similar strategy with turnovers. Looking to calculate a players net turnovers, I took a players steals and subtracted turnovers from them. But once again I followed the lead of the Wages of Wins authors by adding in .7 blocks and subtracting .4 times a players personal fouls.

For my rebounding number, I again used the Wages’ analysis. They say that offensive rebounds are more important that defensive rebounds, so I used .7 and .3 as their respective weighting factors.

From a free throw shooting perspective, it doesn’t do a player any good to shoot 1.000 if he never gets to the line, so the standard measure for evaluating a player’s effectiveness it to look at how many free throws he makes relative to the number of field goal (not free throw) attempts. You improve that number by getting to the line often and making your shots when you do.

So those are the formulas that are the basis for evaluating the four factors. But, so far, what I’ve done doesn’t vary much from the Win Score calculation. Here’s where the twist comes in. What I want to evaluate is how a player stacks up among a select group. Specifically, I start with the top 30 players in the league in terms of minutes played per game, under the theory that if a guy isn’t on the floor for most of the minutes that his team is playing then he’s not very likely to be one of the top guys in the league (as an aside, it also makes my data collection one heckuva lot easier). Then I rank each of the players in each of the four categories. The score that a player gets in each category is based on his rank, not his raw score (the best player of the 30 players in any category gets 10 points, the worst gets .33). All of a sudden the big scoring lead that Gordon has over second place Geary Claxton doesn’t mean quite so much.

But I’m not quite done yet. Dean Oliver says his four factors are not created equal. They each have a relative weight. The specific weighting factors that Oliver suggests are 10 for scoring, 5-6 for turnovers, 4-5 for rebounding, and 2-3 for free throw shooting. I simply take the midpoint for his suggested ranges and apply it to each of the factors. Then as a final step I normalize all the scores to 10.0 (I’m a firm believer in the Bo Derek rating system – no 158.3 perfect quarterback rating for me – a perfect score is 10.0).

So to score well under this system, a player will need to have a complete game. He’ll have a hard time overcoming any deficiency simply by racking up a huge lead in any one factor.

So what does this mean for Eric Gordon? How will his game stand up?

Answer tomorrow.

Using that as a model, here are the top ten performers in the conference (NOTE: for each of these evaluations, I limited my calculations to the top thirty guys in the league in terms of minutes per game).

Game Score

Many, most notably Dean Oliver and John Hollinger, find fault with the simplistic NBA model. They feel that all of these measure should not have equal wait. They modify the NBA model by only assigning half a point to blocks, assists, fouls, and free throw attempts.

Using their model, often called Game Score, the top ten performers are:

Win Score

Lastly there are others, specifically the authors of Wages of Wins, who feel that Oliver and Hollinger were on the right track by assigning weights but were just arbitrary in assigning half weights to certain values. Instead they choose to run a regression analysis to more accurately determine the correct weights. Their conclusions: Points, steals, and turnovers are equal and assigned a weight of 1; field goal attempts, offensive rebounds, assists, and blocks get a .7 weight; made field goals and personal fouls have a .4 factor; and, finally, defensive rebounds weigh in at .3.

Using that formula, called Win Score, here are the top ten in the conference.

Rather than anointing one as better than the others in this post (I have my own methodology that I’m working on that I’ll cover at a later date), I’m just going to average the three approaches.

Doing that give us this top ten.

It certainly looks like Eric Gordon could be on track to win both the Freshman and the Player of the Year in the B10, although I do suspect that DJ White will pick up a bunch of votes for his body of work.

Also, all of the measures seem to be pretty consistent on who the top five are. Things do start to shuffle around a little bit with the second ten though.

It will be interesting to see how things hold up through conference play.

Now the rest of this post will be devoted to quantifying that answer.

First the traditional measures.

In the hoops world the standards of measurement have always been one, scoring, and two, rebounding. Claxton is one of just three Big Ten players (Raymor Morgan and DJ White being the others) to sit in the top ten in both categories.

Here’s the Big Ten Top Ten scoring list:

Here’s the Big Ten Top Ten rebounding list:

Now, if I may, I’m going to borrow from the golf world and come up with a not so traditional measure.

Total Playing:

The PGA stat Total Driving is simply the sum of the players rank in driving distance and driving accuracy (i.e. fairways hit in regulation). Thus my Total Playing statistic simply becomes the sum of the Big Ten players ranking in scoring and rebounding.

So my Total Playing list becomes:

1. Geary Claxton PSU 4 (2 + 2)
2. DJ White IU 7 (6 + 1)
3. Raymor Morgan MSU 8 (3 + 5)