2008-2009 Shocker Men's Prouty Ratings

[RoyalShock]

notice ae is at the bottom on all games.

That's because the players are in order based on their season rating, not their performance for that game. But, yeah, he's had a number of poor games this year.

[1979Shocker]

Wichita State vs TCU Prouty Ratings

              TCU     All 
Player       Game    Games 
Hannah       0.193   0.365
Clemente     0.315   0.332
Hawkins      0.358   0.322
Murry        0.334   0.319
Stutz        0.347   0.317
Kyles*       0.311   0.266
Chamberlain* 0.410   0.264
Durley       0.216   0.252
Hatch        0.016   0.249
Michael      0.000   0.228
Griskenas    0.000   0.197
Ellis        0.299   0.189
Britton      0.000   0.064
Steven       0.000   0.001
TEAM         0.320   0.345

Team Winning Percentage: 0.375

* Improved from previous game.

[1979Shocker]

Looking at all the previous game ratings, Chamberlain has improved nearly every game. Hawkins and Kyles are starting to move up , Durley has dropped some, and Ellis hasn't really improved like we hoped he would. But the season is still young.

[Snapshot9]

1979Shocker - Thanks for doing this. It is the best barometer for analysis in an individual and team level.

So far - We have 5 C's, 4 D's, and 1 F so far this season.

[MadaboutWu]

I have a question on the ratings. Why can an individual have a better game than his overall average and his average still declines? Is it the effect of the loss bringing down his average?

For example vs. TCU Ellis had a .299 rating but his overall rating declined to .189.

[RoyalShock]

I have a question on the ratings. Why can an individual have a better game than his overall average and his average still declines? Is it the effect of the loss bringing down his average?

For example vs. TCU Ellis had a .299 rating but his overall rating declined to .189.

It must be. The same thing is true for Hawkins, Murry, Stutz and Kyles.

[ShockerInKC]

You're right on.
The 'win rating' of the team affects all players scores in a weighted manner. The weight is: how much time did the player play (this is the 'contribution' to the win or loss).

The problem here is, that early in the season every additional game, as long as it doesn't end in a tie (which can't happen in basketball anyway) has a greater affect on the win rating. So when we lost the 'win rating' went from .429 to .375 so it dropped everybody on the team by (.429 - .374) / 4. This is in turn weighted by the % the player played of the whole allotted minutes. So a player who might have a slightly above average game and plays a lot of minutes gets dragged down more by early season losses.

[1979Shocker]

I have a question on the ratings. Why can an individual have a better game than his overall average and his average still declines? Is it the effect of the loss bringing down his average?

For example vs. TCU Ellis had a .299 rating but his overall rating declined to .189.

Yes. Losing affects the Win Rating.

That part of the formula is

Win Rating = MIN / (TEAM MIN / 5) * Team Winning Pct

With a current Team Winning Pct of 0.375 (3/8 ), we need to win tonight or else it drops to 0.333 (3/9). A win will bring it back up to 0.444 (4/9).

Anyone that can go against the grain and move their rating up even in a loss has to have a pretty good game ratings wise. It works the other way too. If your rating goes down and the team still wins, then you know you had a bad game.

[MadaboutWu]

Thanks 1979 and ShockerinKC for the clarification.

[1979Shocker]

Wichita State vs Gardner-Webb Prouty Ratings

              GW      All 
Player       Game    Games 
Hannah*      0.493   0.384
Murry*       0.492   0.352
Clemente*    0.264   0.334
Hawkins*     0.281   0.325
Stutz        0.254   0.316
Griskenas    0.427   0.298
Chamberlain* 0.366   0.286
Durley*      0.414   0.280
Kyles        0.055   0.251
Hatch        0.053   0.235
Michael      0.000   0.228
Ellis*       0.256   0.195
Britton      0.000   0.064
Steven       0.000   0.001
TEAM*        0.404   0.368

Team Winning Percentage: 0.444

* Improved from previous game.

[ShockerInKC]

hey, 1979,
If it isn't too much trouble, can you get the Prouty Ratings of the upcoming opponents? Then we could compare player matchups with the ratings.

thx.

[1979Shocker]

hey, 1979,
If it isn't too much trouble, can you get the Prouty Ratings of the upcoming opponents? Then we could compare player matchups with the ratings.

thx.

I'll see what I can come up with. I'll post Northern Arizona's ratings after their game with Stanford.

[im4wsu]

I have calculated the Plus/Minus points as used by Hockey teams for the nine games so far this season. It is simply the points scored by WSU minus points scored by the opponents while the player was in the game--it doesn't make any difference whether the player or a teammate scores the points.

For the team, we've outscored the opponents by 3.1 per game. The right-most column gives the player's equivalent assuming he played the entire 40 minutes.

Aside from those who haven't played, Hatch and Durley are the team leaders; joined by Murry, Hannah, Clemente & Stutz all above the team average.



Through nine games, there have been 90 combinations of players on the floor. Only six of these lineups have played more than 10 minutes. Here are the plus/Minus calculations for the units:

Points per
40 Minutes Minutes
36.0 12 Hatch, Chamberlain, Kyles, Durley and Ellis
13.5 18 Hannah, Clemente, Hatch, Murry and Durley
12.2 53 Hannah, Clemente, Murry, Hawkins and Stutz
10.5 15 Clemente, Chamberlain, Murry, Hawkins and Stutz
00.0 47 Hannah, Clemente, Murry, Hawkins and Durley
-41.6 11 Hannah, Clemente, Murry, Ellis and Stutz

[RoyalShock]

That first team with Hatch, Chamberlain and Kyles seems like a statistical outlier due to a couple of late-game situations.

The 2nd team looks like our best option, but with Hawkins playing better and Hatch having trouble scoring, maybe not.

That leaves team 3 - CH, RC, TM, AH and GS.

[ShockerInKC]

The points per minutes per lineup is handy except for the hidden outliers. (like the guy who comes in at the end of the game and hits 2 free throws and ranks at the top of the %).

I would agree with Royal on the assessment of team #1, if you made this a density function..............

Whoops....

Try multiplying the points per minute of the lineups times the number of minutes (so it's just points) and that will be a measure of the lineup that scores the most points. Then do a side-by-side so you can detect the little-used but high-efficiency lineups.

The other thing this does is it shows if the coach is using 'too much' of an in-efficient lineup.