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2 Stats have Plagued W&M MBB This Season
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WMSportsBlog Offline
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Post: #1
2 Stats have Plagued W&M MBB This Season
When W&M finishes with 12.9 or LESS of these, it's 3-0 in CAA play -- but when it finishes with 12.9 or MORE of these, the team is 0-3 in CAA play.

Can you guess what it is?

2 stats have PLAGUED the team so far this year. Check out our analysis in the below article.

Hoping we can get these turned around soon.

GO TRIBE.

Link: https://wmsportsblog.com/2019/01/16/10136/
01-16-2019 05:36 PM
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TribePride91 Offline
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RE: 2 Stats have Plagued W&M MBB This Season
Nice article guys especially for those who are uninformed and just starting the follow the Tribe this season. But, the CAA tourney is always in March, not February.

I tend to look at the conference season week by week. Week 1 was good, week 2 was decent, week 3 was bad. We definitely need this week and next to be good(3-0) or we are likely out of the race for a 1 or 2 seed already. No reason to expect it would take less than 10 to 11 wins to finish in the top 4 and the Tribe closes with 5 of 7 on the road. Basically, if they are less than 7-4 following the Gold Rush game in early Feb., there is no shot of that. That means 4-1 over the next five(at the worst). None of this means that they couldn't make a run in March, but history says it won't happen if they don't start winning long before then.
01-17-2019 11:17 AM
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TribeNiner Offline
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RE: 2 Stats have Plagued W&M MBB This Season
Okay guys, bear with me here, but I wanted to crunch some numbers on our last 10 years.

First I wanted to look at our adjusted offense and defense rankings (based on who you play and how you play) against our rank and win percentage (all pulled from KenPom). Generally, these give you some idea of how we've fared over the last decade.

[Image: Excel-WL.png]

I decided to plug these numbers into a basic OLS regression to see what sort of results we got.

For win percentage (because rank is a bit tautological), we get the following models:

Defense only:

Win% = .6868 - (.0007*defensive rank)

This doesn't show as statistically significant (which could be due to the small number of observations at only 10, and because of the lack of variation in our rank not giving us enough information), but it does suggest that higher defensive rank (which is a worse defense in this case) correlates to a lower win percentage. By way of example, if we're ranked 100th in defense that formula would give us an expected win percentage of .6168, while a defensive rank of 300 would give us an expected win percentage of .4768.

Offense Only:

Win% = .6990 - (.002*offensive rank)

This is statistically significant at the .01 level. This means that if we're ranked 100th in offense, we would have an expected win percentage of .499, while an offensive rank of 50th would give us an expected win percentage of .599.

These are simple models, so I added a little extra (still super simple) to them. Somewhat intuitively it could be that we need to account for both of these items at the same time. Consider that if we look at what happens when we control for the other we get the following model:

Win% = .7966 - (.002*offensive rank) - (.0004*defensive rank)

This has an adjusted r-squared of .9296 (which basically just says offensive rank and defensive rank somewhat obviously explain a lot of what is going on with the win %). Again, offense is statistically significant while defense is not, ceteris paribus.

So an offensive rank of 159 and a defensive rank of 280 like we have this year would yield an expected win percentage of .367. Our actual win percentage this year is .389. That's not a bad prediction for a simplistic model.

Further, I thought it might be interesting, rather than holding changes in offensive and defensive rank at zero while examining the other, to look at how the combined total of offensive rank and defensive rank helped explain win % (so a rank of 100 in offense and a rank of 200 in defense, is a total rank of 300).

Running that regression, we get the following model:

Win% = 1.0062 - (.0014*total rank)

This variable is statistically significant, as well. So plugging in this year's numbers we would have an expected win percentage this year of .3916 (against an actual of .389).

[Image: WinTotal.png]

The ultimate story here, depending on which model might hold up more strongly over the long run (if either), would be that the last model suggests if we could get our total ranks below about 350, then we should expect a winning record.

Just food for thought because I had an hour on my hands here at lunch.

Edit: fix image links
(This post was last modified: 01-17-2019 12:37 PM by TribeNiner.)
01-17-2019 12:29 PM
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tribe64 Offline
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Post: #4
RE: 2 Stats have Plagued W&M MBB This Season
(01-17-2019 12:29 PM)TribeNiner Wrote:  Okay guys, bear with me here, but I wanted to crunch some numbers on our last 10 years.

First I wanted to look at our adjusted offense and defense rankings (based on who you play and how you play) against our rank and win percentage (all pulled from KenPom). Generally, these give you some idea of how we've fared over the last decade.

[Image: Excel-WL.png]

I decided to plug these numbers into a basic OLS regression to see what sort of results we got.

For win percentage (because rank is a bit tautological), we get the following models:

Defense only:

Win% = .6868 - (.0007*defensive rank)

This doesn't show as statistically significant (which could be due to the small number of observations at only 10, and because of the lack of variation in our rank not giving us enough information), but it does suggest that higher defensive rank (which is a worse defense in this case) correlates to a lower win percentage. By way of example, if we're ranked 100th in defense that formula would give us an expected win percentage of .6168, while a defensive rank of 300 would give us an expected win percentage of .4768.

Offense Only:

Win% = .6990 - (.002*offensive rank)

This is statistically significant at the .01 level. This means that if we're ranked 100th in offense, we would have an expected win percentage of .499, while an offensive rank of 50th would give us an expected win percentage of .599.

These are simple models, so I added a little extra (still super simple) to them. Somewhat intuitively it could be that we need to account for both of these items at the same time. Consider that if we look at what happens when we control for the other we get the following model:

Win% = .7966 - (.002*offensive rank) - (.0004*defensive rank)

This has an adjusted r-squared of .9296 (which basically just says offensive rank and defensive rank somewhat obviously explain a lot of what is going on with the win %). Again, offense is statistically significant while defense is not, ceteris paribus.

So an offensive rank of 159 and a defensive rank of 280 like we have this year would yield an expected win percentage of .367. Our actual win percentage this year is .389. That's not a bad prediction for a simplistic model.

Further, I thought it might be interesting, rather than holding changes in offensive and defensive rank at zero while examining the other, to look at how the combined total of offensive rank and defensive rank helped explain win % (so a rank of 100 in offense and a rank of 200 in defense, is a total rank of 300).

Running that regression, we get the following model:

Win% = 1.0062 - (.0014*total rank)

This variable is statistically significant, as well. So plugging in this year's numbers we would have an expected win percentage this year of .3916 (against an actual of .389).

[Image: WinTotal.png]

The ultimate story here, depending on which model might hold up more strongly over the long run (if either), would be that the last model suggests if we could get our total ranks below about 350, then we should expect a winning record.

Just food for thought because I had an hour on my hands here at lunch.

Edit: fix image links

Wow, you really need a hobby!
01-17-2019 09:02 PM
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WMtribe17 Offline
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Post: #5
RE: 2 Stats have Plagued W&M MBB This Season
I think the 12.9 turnovers we average is actually worse than the stats show. I feel like last year a lot of our turnovers were deadball turnovers, which doesn't result in easy transition points. I feel like the opposite is true this year - just a ton of lazy, inefficient passes along the perimeter for long stretches of time leads to turnovers and easy buckets for the other team.

While our defense is slightly better than last year (mainly because we aren't having to stick to a 6 man rotation), the offensive inefficiency has been very worrying to me (and TribeNiner's data unfortunately backed up my perception of how bad we have been on offense). We just don't have many players that can create their own shot/shots for others combined with not having sharpshooters at most/all positions on the floor, which makes us an easier team to defend against.
(This post was last modified: 01-18-2019 12:36 AM by WMtribe17.)
01-18-2019 12:35 AM
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Blow Gym rat Offline
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Post: #6
RE: 2 Stats have Plagued W&M MBB This Season
(01-17-2019 09:02 PM)tribe64 Wrote:  Wow, you really need a hobby!

I think he pretty clearly has one, even if it's not the one I would have chosen for myself. (Model trains, anyone?)
01-18-2019 09:49 AM
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TribalBeaver Offline
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Post: #7
RE: 2 Stats have Plagued W&M MBB This Season
It is disconcerting when Scott passes the ball with 1 second on the shotclock or Milon turns it over in the final 2 minutes or Pierce flings up a double teamed runner down the stretch. The team just does not appear disciplined this year in any sense of the word. You could blame that on youth but I would say Owens has been the most disciplined player on the court and Audige minus how much he talks on the court generally plays calm.
01-18-2019 10:52 AM
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RE: 2 Stats have Plagued W&M MBB This Season
Responding to the original blog post, only 7 of 353 teams are averaging under 10 turnovers, so being in single-digits a quarter of the time is hard. Moreso than turnovers committed, you're worse at forcing turnovers at only 10.5 per game. You were tied for 344th of 351 before yesterday. The NCAA hasn't updated their statistics to include yesterday, and they list 351 teams unfortunately excluding California Baptist and North Alabama for being new to Division I. If it's any consolation, Michigan State is fifth in the NET while forcing the fifth fewest turnovers per game.

Thank you TribeNiner for the statistics. There are 353 teams in Division I, and the average for the ten seasons you looked at would be a little under 350. With 353 teams, a team that was in opposite ranks in offense and defense, such 100th best and 100th worst, would have a sum of 354. Therefore a sum of 350 being .500 is expected without regression equations. It's nice to see regression agree with that. If regression had showed that a sum of 400 would be .500, I would wonder why. There are times in math when there are multiple ways of doing the same thing where one method requires more advanced math.

It is unique that offense matters more than defense, and I would be interested in knowing if that's true for a majority of teams or if they are equally important. I'm never going to find out because nobody will do what you did for all 353 teams.

I did correlations between Hofstra's statistics differentials and winning percentage for their 17 complete seasons in the CAA. In all cases, I made good numbers positive. I made fouls and turnovers positive if Hofstra had fewer than their opponents. Of 16 variables, the only one that produced a negative correlation was rebound differential per game, albeit -.1005 is weak and does not mean the Hofstra would have been better off with a worse rebound differential per game and everything else the same. Foul differential per game had a negligible correlation of .0008. Points per game differential had the strongest correlation at .8848. For those who don't know, correlation ranges from -1 to 1. I plan on doing a regression equation. I found a website that can handle 11 independent variables, so I'm going to use my 11 variables with the strongest correlation.
(This post was last modified: 01-18-2019 03:21 PM by EvanJ.)
01-18-2019 03:09 PM
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TribeNiner Offline
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RE: 2 Stats have Plagued W&M MBB This Season
(01-18-2019 03:09 PM)EvanJ Wrote:  Thank you TribeNiner for the statistics. There are 353 teams in Division I, and the average for the ten seasons you looked at would be a little under 350. With 353 teams, a team that was in opposite ranks in offense and defense, such 100th best and 100th worst, would have a sum of 354. Therefore a sum of 350 being .500 is expected without regression equations. It's nice to see regression agree with that. If regression had showed that a sum of 400 would be .500, I would wonder why. There are times in math when there are multiple ways of doing the same thing where one method requires more advanced math.

It is unique that offense matters more than defense, and I would be interested in knowing if that's true for a majority of teams or if they are equally important. I'm never going to find out because nobody will do what you did for all 353 teams.

I did correlations between Hofstra's statistics differentials and winning percentage for their 17 complete seasons in the CAA. In all cases, I made good numbers positive. I made fouls and turnovers positive if Hofstra had fewer than their opponents. Of 16 variables, the only one that produced a negative correlation was rebound differential per game, albeit -.1005 is weak and does not mean the Hofstra would have been better off with a worse rebound differential per game and everything else the same. Foul differential per game had a negligible correlation of .0008. Points per game differential had the strongest correlation at .8848. For those who don't know, correlation ranges from -1 to 1. I plan on doing a regression equation. I found a website that can handle 11 independent variables, so I'm going to use my 11 variables with the strongest correlation.

It definitely varies from the 350 based on individual teams, and so far (anecdotally) it appears to be by conference. That makes some theoretical sense, as a team in the ACC can’t expect middling ratings to lead to a .500 record, while a team in a lower end conference could probably win a good number of games without exceedingly good rankings.

I haven’t done enough to give more definitive answers as to how much it varies from one conference to another (or if that observation would continue to hold), but that’s what some of my early findings seem to show.
(This post was last modified: 01-18-2019 03:25 PM by TribeNiner.)
01-18-2019 03:21 PM
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EvanJ Offline
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RE: 2 Stats have Plagued W&M MBB This Season
(01-18-2019 03:21 PM)TribeNiner Wrote:  It definitely varies from the 350 based on individual teams, and so far (anecdotally) it appears to be by conference. That makes some theoretical sense, as a team in the ACC can’t expect middling ratings to lead to a .500 record, while a team in a lower end conference could probably win a good number of games without exceedingly good rankings.

I haven’t done enough to give more definitive answers as to how much it varies from one conference to another (or if that observation would continue to hold), but that’s what some of my early findings seem to show.
It varies by SOS, which is related to your conference, but that doesn't make the mean for every team not be about 350. There will be teams with hard SOS that were below .500 with a sum near 350 (or near any specific number) and teams with easy SOS that were above .500 with a sum near 350 that balance out. If each team's SOS was rated by an amount of points per game easier or harder than average, the mean of the 353 teams would be near 0. It wouldn't be exactly 0 because teams with harder schedules play more postseason games. It would be 0 if every team played the same amount of games. If you wanted to use a team's offensive ranking, defensive ranking, and SOS to predict their winning percentage, you should use https://www.teamrankings.com/ncb/team-stats/ which only includes game between two Division I teams because non-Division I teams improve your statistics without changing your SOS. Another factor is SOS in and out of a team's conference. You could so something else only including CAA games and see what sum would produce a 9-9 record, but I don't know if your data source can provide subgroups like conference games and home games or just the whole season.
01-21-2019 05:17 PM
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TribeNiner Offline
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RE: 2 Stats have Plagued W&M MBB This Season
(01-21-2019 05:17 PM)EvanJ Wrote:  
(01-18-2019 03:21 PM)TribeNiner Wrote:  It definitely varies from the 350 based on individual teams, and so far (anecdotally) it appears to be by conference. That makes some theoretical sense, as a team in the ACC can’t expect middling ratings to lead to a .500 record, while a team in a lower end conference could probably win a good number of games without exceedingly good rankings.

I haven’t done enough to give more definitive answers as to how much it varies from one conference to another (or if that observation would continue to hold), but that’s what some of my early findings seem to show.
It varies by SOS, which is related to your conference, but that doesn't make the mean for every team not be about 350. There will be teams with hard SOS that were below .500 with a sum near 350 (or near any specific number) and teams with easy SOS that were above .500 with a sum near 350 that balance out. If each team's SOS was rated by an amount of points per game easier or harder than average, the mean of the 353 teams would be near 0. It wouldn't be exactly 0 because teams with harder schedules play more postseason games. It would be 0 if every team played the same amount of games. If you wanted to use a team's offensive ranking, defensive ranking, and SOS to predict their winning percentage, you should use https://www.teamrankings.com/ncb/team-stats/ which only includes game between two Division I teams because non-Division I teams improve your statistics without changing your SOS. Another factor is SOS in and out of a team's conference. You could so something else only including CAA games and see what sum would produce a 9-9 record, but I don't know if your data source can provide subgroups like conference games and home games or just the whole season.

NVM. Not typing it all out from my phone, but basically, I’m concerned with what our efficiency needs to be to do well in the CAA. Of course all teams average out to the mean across all of basketball.
(This post was last modified: 01-21-2019 09:43 PM by TribeNiner.)
01-21-2019 09:40 PM
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