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Statistical Analysis of Team So Far

As of 02/13
1. Dalton .86
2. James 0.84
3. Moemeka 0.71
4. Adams 0.67
5. Naughton 0.63
6. Herndon 0.61
7. Aka Gorski 0.54
8. JMac 0.51
9. Marshall 0.51
10. Barnes 0.49
11. Kelley 0.44
12. Lieberman 0.37
13. 0.36

16729142_10100934667855373_1013562760947613485_n.jpg
 
As of 02/15

1. Dalton 0.85
2. James 0.85
3. Adams 0.69
4. Moemeka 0.67
5. Herndon 0.61
6. Naughton 0.61
7. Aka Gorski 0.53
8. JMac 0.51
9. Marshall 0.51
10. Barnes 0.49
11. Kelley 0.44
12. Lieberman 0.38
13. Averbuck 0.36

16730540_10100936302973583_4533551552178142946_n.jpg
 
As of 02/22

1. Dalton 0.85
2. James 0.85
3. Moemeka 0.72
4. Adams 0.71
5. Herndon 0.62
6. Naughton 0.61
7. Aka Gorski 0.54
8. Marshall 0.52
9. JMac 0.50
10. Barnes 0.49
11. Kelley 0.44
12. Lieberman 0.38
13. Averbuck 0.36

16865173_10100942777922733_1937552150293239684_n.jpg


***QUICK NOTE*** There is a discrepancy with Moemeka. In the last round of data his PPPMP was .67. Upon inspection I realized I had fat fingered his entry resulting in a skewed figure. This resulted in the sharp spike this round. If my math serves, last time he would have been at 0.71 so he did still improve this round.
 
I would love to see Dalton, Herndon, Adams, James and Moemeka out there for 5-6 minutes in a row. What have we got to lose?
 
LanderPoke said:
I would love to see Dalton, Herndon, Adams, James and Moemeka out there for 5-6 minutes in a row. What have we got to lose?
Click to expand...
That's just crazy talk. How could we possibly put the most athletic lineup on the floor? You don't know anything..... :thumb:
 
Wyolie Coyote said:
LanderPoke said:
I would love to see Dalton, Herndon, Adams, James and Moemeka out there for 5-6 minutes in a row. What have we got to lose?
Click to expand...
That's just crazy talk. How could we possibly put the most athletic lineup on the floor? You don't know anything..... :thumb:
Click to expand...

You know what's funny is I think we've seen JMac, Gorski, Lieberman, Naughton and Herdon before. I think Edwards had played JMac, Kelly and Lieberman together, too, for a tiny bit.
 
As of 02/27

1. James 0.86
2. Dalton 0.83
3. Adams 0.71
4. Moemeka 0.71
5. Herndon 0.62
6. Naughton 0.61
7. Aka Gorski 0.53
8. Barnes 0.53
9. Marshall 0.52
10. JMac 0.51
11. Kelley 0.42
12. Lieberman 0.39
13. Averbuck 0.36

16998905_10100946819952473_3877819286623202144_n.jpg
 
ItSucksToBeACSURam said:
In an attempt to not completely derail the Tweets thread, I will repost this here....

So.... I have really been wracking my brain trying to figure out an actual reason why Edwards refuses to play his best players most. I am in no way a statistician but can run a spreadsheet with the best of them. I want to understand why JJ and Dalton don't play the minutes we the fans believe they should. I figured there was something in the stats that were missing. Here is a breakdown of what I found.

Through January 13th (17 Games)

Minutes Per Game:
1. JMac 33.6
2. Herndon 30.6
3. Dalton 26.3 (Surprised he was this high)
4. Aka Gorski 25.7
5. James 24.5

Points Per Game:
1. James 15.6 (Duh) (0.64 per minute played)
2. JMac 13.4 (this too surprised me with his recent struggles) (0.40)
3. Dalton 13.2 (would have figured this would have been higher) (0.50)
4. Herndon 10.4 (0.34)
5. Aka Gorski 9.4 (0.37)

Rebounds Per Game:
1. Dalton 8.7 (Monster) (0.33 per minute played)
2. Herndon 6.1 (0.20)
3. James 4.8 (0.20)
4. Naughton 3.7 (0.28)
5. JMac 3.6 (0.11)

Assist Per Game:
1. Dalton 3.2 (0.12 per minute played)
2. Jmac 2.6 (0.08)
3. Aka Gorski 2.2 (0.09)
4. Lieberman 2.1 (0.11)
5. James 1.7 (0.07)

Steals Per Game:
1. James 0.9 (0.04 per minute played)
2. Lieberman 0.8 (0.04)
3. Dalton 0.7 (0.03)
4. JMac 0.6 (0.02)
5. Herndon 0.6 (0.02)

Blocks Per Game:
1. Herndon 2.1 (0.07 per minute played)
2. Dalton 0.9 (0.03)
3. James 0.6 (0.02)
4. Moemeka 0.5 (0.08)
5. Naughton 0.4 (0.03)

Turnovers Per Game:
1. Dalton 2.5 (0.1 Per minute played)
2. James 2.1 (0.09)
3. Adams 1.8 (0.13)
4. Herndon 1.8 (0.06)
5. Lieberman 1.6 (0.08)

I compiled stats for every roster player and these were the top 5 in each.

I under stand that TOs are not great and that they have a negative effect on the game. I took that into account. I took each of the Per Minute stats per each player and came up with a number that I am calling Positive Plays Per Minute. Kind of like WHIP in baseball, this is telling me, PER MINUTE, what kind of impact each player is making. Ultimately, the higher the number the better. Here it is:

Positive Plays Per Minute Played (PPPMP):
1. Dalton 0.92
2. James 0.88
3. Naughton 0.69
4. Adams 0.65
5. Moemeka 0.63
6. Herndon 0.60
7. Marshall 0.57
8. JMac 0.56
9. Kelley 0.55
10. Aka Gorski 0.52
11. Barnes 0.51
12. Averbuck 0.36
13. Lieberman 0.34

SO what does all of this mean? This has to be taken at face value but it fairly obvious that James and Dalton are TOO important to not be receiving the most minutes. It is also interesting that Moemeka and Naughton are making a lot of noise with their reduced minutes. Poor Lieberman seems to play a lot of empty (or worse yet, negative) minutes.

AGAIN: I am not statistician, I am just a Pokes fan with Excel and ESPN. Take this as you will, but I think its rather interesting that the stats show definitively that the two favorites on this board actually are the our best statistical players and have the biggest impact on the game when they are subbed in. I will do what I can to update the PPPMP as the season pushes on. Let the conversations begin.
Click to expand...

Thank you for your hard work. I know how hard it sometimes is to transfer data like this if websites are not really formatted to move things to Excel.

I do think that another thing to think about are the number of foolish or bad defensive plays each player makes that do not really show up in the statistics. For example, how many easy buckets does a player give up to an opponent because our player lost track of where he was supposed to be or made a dumb (not hustle or strategic) foul, or worst of all, took a play off? The same thing can be said of rebounds not gotten because of poor rebounding position. Those are the kind of things that make coaches grumpy with their scorers. A guy that puts up 20 per game but gives up 21 actually had a net-negative of 1 point for the game. It is better to score a mere 7 points but only give up 6 as a player, is it not? Is it better for five players to score 100 points but give up 105? Or is it better for five players to score a mere 35 but only give up 30 in the game?
 
I wish there was someone out there that tracked player +/- in games. It is not the end all statistic but I would be interested in seeing those.
 
WyoExpat said:
ItSucksToBeACSURam said:
In an attempt to not completely derail the Tweets thread, I will repost this here....

So.... I have really been wracking my brain trying to figure out an actual reason why Edwards refuses to play his best players most. I am in no way a statistician but can run a spreadsheet with the best of them. I want to understand why JJ and Dalton don't play the minutes we the fans believe they should. I figured there was something in the stats that were missing. Here is a breakdown of what I found.

Through January 13th (17 Games)

Minutes Per Game:
1. JMac 33.6
2. Herndon 30.6
3. Dalton 26.3 (Surprised he was this high)
4. Aka Gorski 25.7
5. James 24.5

Points Per Game:
1. James 15.6 (Duh) (0.64 per minute played)
2. JMac 13.4 (this too surprised me with his recent struggles) (0.40)
3. Dalton 13.2 (would have figured this would have been higher) (0.50)
4. Herndon 10.4 (0.34)
5. Aka Gorski 9.4 (0.37)

Rebounds Per Game:
1. Dalton 8.7 (Monster) (0.33 per minute played)
2. Herndon 6.1 (0.20)
3. James 4.8 (0.20)
4. Naughton 3.7 (0.28)
5. JMac 3.6 (0.11)

Assist Per Game:
1. Dalton 3.2 (0.12 per minute played)
2. Jmac 2.6 (0.08)
3. Aka Gorski 2.2 (0.09)
4. Lieberman 2.1 (0.11)
5. James 1.7 (0.07)

Steals Per Game:
1. James 0.9 (0.04 per minute played)
2. Lieberman 0.8 (0.04)
3. Dalton 0.7 (0.03)
4. JMac 0.6 (0.02)
5. Herndon 0.6 (0.02)

Blocks Per Game:
1. Herndon 2.1 (0.07 per minute played)
2. Dalton 0.9 (0.03)
3. James 0.6 (0.02)
4. Moemeka 0.5 (0.08)
5. Naughton 0.4 (0.03)

Turnovers Per Game:
1. Dalton 2.5 (0.1 Per minute played)
2. James 2.1 (0.09)
3. Adams 1.8 (0.13)
4. Herndon 1.8 (0.06)
5. Lieberman 1.6 (0.08)

I compiled stats for every roster player and these were the top 5 in each.

I under stand that TOs are not great and that they have a negative effect on the game. I took that into account. I took each of the Per Minute stats per each player and came up with a number that I am calling Positive Plays Per Minute. Kind of like WHIP in baseball, this is telling me, PER MINUTE, what kind of impact each player is making. Ultimately, the higher the number the better. Here it is:

Positive Plays Per Minute Played (PPPMP):
1. Dalton 0.92
2. James 0.88
3. Naughton 0.69
4. Adams 0.65
5. Moemeka 0.63
6. Herndon 0.60
7. Marshall 0.57
8. JMac 0.56
9. Kelley 0.55
10. Aka Gorski 0.52
11. Barnes 0.51
12. Averbuck 0.36
13. Lieberman 0.34

SO what does all of this mean? This has to be taken at face value but it fairly obvious that James and Dalton are TOO important to not be receiving the most minutes. It is also interesting that Moemeka and Naughton are making a lot of noise with their reduced minutes. Poor Lieberman seems to play a lot of empty (or worse yet, negative) minutes.

AGAIN: I am not statistician, I am just a Pokes fan with Excel and ESPN. Take this as you will, but I think its rather interesting that the stats show definitively that the two favorites on this board actually are the our best statistical players and have the biggest impact on the game when they are subbed in. I will do what I can to update the PPPMP as the season pushes on. Let the conversations begin.
Click to expand...

Thank you for your hard work. I know how hard it sometimes is to transfer data like this if websites are not really formatted to move things to Excel.

I do think that another thing to think about are the number of foolish or bad defensive plays each player makes that do not really show up in the statistics. For example, how many easy buckets does a player give up to an opponent because our player lost track of where he was supposed to be or made a dumb (not hustle or strategic) foul, or worst of all, took a play off? The same thing can be said of rebounds not gotten because of poor rebounding position. Those are the kind of things that make coaches grumpy with their scorers. A guy that puts up 20 per game but gives up 21 actually had a net-negative of 1 point for the game. It is better to score a mere 7 points but only give up 6 as a player, is it not? Is it better for five players to score 100 points but give up 105? Or is it better for five players to score a mere 35 but only give up 30 in the game?
Click to expand...

My hope is that for next season to have a much more comprehensive spreadsheet built that goes a little further in depth than this one. I want to track things like PER (Player Efficiency Rating), Points Produced, +/- per game or minute played, and some other more analytical stats that drill down deeper than what I have here. I have really enjoyed this new side of being a member of Wyonation and I look to expand on it for next season. I am slowly starting to work on a new spreadsheet, theres just no way it will be ready this year.
 
LanderPoke said:
I'd like to see how James and Dalton compare to other top dogs in the MWC using your metric
Click to expand...

This is going to be long....

TL:DR - James is in the top 4 in the MWC in PPPMP

This first graph is the highest PPPMP player on each team. There were a couple surprises here, none moreso than Clarke on San Jose State being at 0.96. That is insane to be performing at that high of a level, to be that valuable, this late in the season. And its not like his numbers are skewed by playing time or a few outlier monster performances. The dude is averaging 31.6 minutes a game, 17.7 points per game, 8.5 rebounds per game, and only 1.8 TO per game. That dude is having a great season.

16998656_10100948267850873_3889971383279944439_n.jpg


The second graph is showing the top 7 players on each team in the MWC. I was most surprised by the Cowboys honestly. We are at or near the top at our top 7 positions but it hasn't necessarily translated to more wins. CSU is top loaded with their top 3 and much lower the other 4 yet they have solidified into a very solid unit and are winning. Same with Nevada. They only have one single player above .80 and 4 at .62 or lower but they are winning at an impressive clip.

16998059_10100948267855863_7016632915117832967_n.jpg


Honestly, this exercise has shown, to me, that there is some element at play this season that we are not picking up on that is leading to more wins than some of the on court stats included in my PPPMP. Be it coaching, be it officiating, be it luck I don't know. Presenting these graphs to an outsider with NO knowledge of the MWC this season may lead them to believe certain teams may have more wins than they do (WYOMING, UNM) and that some teams may not be winning the games that they are (CSU, UNR).

I am really interested in your thoughts on these.


*** NOTE *** These figures DO not reflect any games played last night (2/28). I am sure these number may change a tad. James played a pretty good game.
 
Very interesting! It's weird that we don't have more wins when presented with this data. two thoughts:

1. It would be nice to see conference only stats. We had a few games in the pre-season (USC) that are skewing the stats in a good way for us.

2. Rebounding might be the issue for us. Specifically O rebounding. I know we are DFL in the conference in O rebounding.

additional thought.. It might be worth it to take a look further down the line at the 8th, 9th, 10th and even 11th best players on each team. I'm pretty sure we play our crappy players a whole lot more than other teams. It's got to be a minutes distribution thing. Pretty much what a few of us have been saying all year.

yup makes sense. Our 10th and 12th best players (JMac and Lieberman) play a combined 60 min per game. Mystery solved.
 
Without further knowledge I would suspect you are being misled by Wyomings relatively high positive plays per minute stat, we play at one of the fastests tempos, so there are more opportunities in a given game for us to produce positive plays. This doesn't take into account how efficient we are at making those positive plays. I do enjoy advanced metrics quite a bit though. There are some others that you should incorporate as well.
 
calpoke25 said:
Without further knowledge I would suspect you are being misled by Wyomings relatively high positive plays per minute stat, we play at one of the fastests tempos, so there are more opportunities in a given game for us to produce positive plays. This doesn't take into account how efficient we are at making those positive plays. I do enjoy advanced metrics quite a bit though. There are some others that you should incorporate as well.
Click to expand...

This is going to get an overhaul before next season. I have greatly enjoyed doing this but I agree with you that this is only one stat and isnt showing the whole picture. I going to work on some efficiency breakdowns, +/- breakdowns etc. for next season.

I'd be interested in the stats you would incorporate
 
The dead horse has been beat all damn year long, but the simple reason that we don't have as many wins as this metric might show we should have is that all year long the coaching staff has outright refused to play its best players, or has played it's best players inconsistently. It's not freaking rocket science. We could have the 5 best players in america on our team, but if we're gonna leave them on the bench and play Donald Duck, Mickey Mouse, and the 3 Stooges instead, the results will be predictable.

Edit: Also wanted to show my appreciation to itsuckstobeacsuram for the charts as well, fantastic job all year, and very well done on these latest ones comparing across the conference!! :thumb:
 
Using possessions instead of minutes might give a more useful graph. You could use an average minutes per possesion multiplier to get a rough estimate of each stat you put up before to see if faster pace teams like UW fall below slower pace teams. It obviously won't be 100% accurate since you are basing it on an average pace of play across the board, but it might produce a useful graph.
 
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