Here is SoS and OBP for the Top 20 ISR teams:

SoS OBP
South Carolina 75 0.372
Houston 36 0.372
Texas 14 0.352
Florida State 63 0.414
Cal Poly 98 0.388
Rice 15 0.354
Louisiana-Lafayette 185 0.412
Vanderbilt 61 0.372
Nevada-Las Vegas 34 0.367
Kentucky 30 0.417
Oregon 20 0.359
Virginia 158 0.384
UC Santa Barbara 139 0.407
San Diego State 49 0.393
Oregon State 101 0.4
Washington 69 0.382
Mississippi 85 0.371
Texas Tech 57 0.386
Tennessee 118 0.378
Pepperdine 84 0.342

If you graph this, there is a clear visual correlation between SoS and OBP. Linear regression yields:
Predicted OBP = (SoS x 0.0002) + 0.366 (r-squared = 0.2). The r-squared is low, but this just means that there are a lot of factors in addition to SoS that affect OBP. The low r-squared does not mean that SoS is not important.

If you rank the top 20 teams based on the difference between predicted OBP and actual OBP, Rice is third worst (ahead of Texas and Pepperdine). This suggests that either i) Rice's OBP is, in fact, worse than most other Top 20 teams after correcting for differences in SoS or ii) the relationship between SoS and OBP is not linear. The later possibility is supported by comparing Rice's stats directly with other teams that have a very high SoS, as I have done already.

This is as nerdy as I can get on a Sunday morning. I'll stop now.

Thanks Temchugh.

Your work is way above my head. I have no doubt about its valdity. But I really appreciate the passion of the argument.

Guess that's why they PLAY te game.

(03-30-2014 09:53 AM)temchugh Wrote:  The r-squared is low, but this just means that there are a lot of factors in addition to SoS that affect OBP. The low r-squared does not mean that SoS is not important.

what is the t statistic for SOS?

(03-30-2014 08:34 AM)waltgreenberg Wrote:  CSF and Stanford are two other teams with known offensive deficiencies (but elite-level pitching in the case of the Titans). Contrary to CSF, Stanford, UT and Oregon, we were supposed to have an above-average offensive club. We don't at this point in the season, and our lack of plate discipline only compounds the problem.

I think the point that's being made is this

"known offensive deficiencies" in some cases is a euphemism for a very tough schedule.

Put another way, the single season HR record for a clean player (non-steroids) is still more than 70 HR's. However it was set in a Class D league over 50 years ago by a player that (and I may be confusing him with another slugger who also hit well over 60 at sub-major league levels) either never got an at bat in the majors, or whose major league career was markedly uneventful.

(03-30-2014 08:37 AM)waltgreenberg Wrote:  

(03-30-2014 08:31 AM)Frizzy Owl Wrote:  Rice had only one walk in yesterday's game, but 16 hits. Surely a high hit total helps explain the low hit total. 8 runs and 16 hits isn't struggling. Game 1 was a struggle, but not yesterday.

Sorry, but a high number of hits do not help explain the meager walk total...and as I mentioned in my post-game post yesterday, 8 runs on 16 hits (5 for extrabases) and 4 errors by the opposing team is not indicative of an efficient offense. To the contrary. I believe only 3 of our runs yesterday were earned...and only one on Friday.

On the other hand, umpires with a wider than normal strike zone . . .

or pitchers with better than average control/command (I don't want to go into that discussion)

can certainly explain why individual game walk totals could vary substantially.

(03-30-2014 08:34 AM)waltgreenberg Wrote:  CSF and Stanford are two other teams with known offensive deficiencies (but elite-level pitching in the case of the Titans). Contrary to CSF, Stanford, UT and Oregon, we were supposed to have an above-average offensive club. We don't at this point in the season, and our lack of plate discipline only compounds the problem.

This is where you and I disagree. This year's offense is average at best, and most likely below average. I don't understand the misplaced optimism that was evident during the preseason that led people to believe this offense would be above average. It is a year older and it added Reeves, in my mind that gave it a potential to strive to be average. With the defense and pitching the Owls have gotten, the average offense works pretty well. The "lack of plate discipline" is what it has been for years. I don't know why the expectations were that it would magically change this year, other than it was a new year.

BTW, there is nothing wrong with an average offense. You can't recruit to be the best in all three phases, in my opinion. Rice has traditionally been a pitching first, and strong up the middle recruiter for years and has done so with great success.

Rice made it to the Super Regionals with this offense last year. If they played those Super Regional games 100 times, Rice goes to Omaha most years.

First one to three wins. That is all you need from this offense.

(03-30-2014 10:51 AM)MemOwl Wrote:  

(03-30-2014 09:53 AM)temchugh Wrote:  The r-squared is low, but this just means that there are a lot of factors in addition to SoS that affect OBP. The low r-squared does not mean that SoS is not important.

what is the t statistic for SOS?

Sure, make me go to the "data analysis" package in Excel.

For SoS as a predictor of OBP, the p-value is almost exactly 0.05 (p = 0.05004 according to Excel). In other words, there is a 5% chance that the apparent relationship between SoS and OBP is due to random chance. If you account for the fact that I predicted a positive relationship (OBP increases with increasing SoS) and observed a positive relationship, the probability drops to 2.5%.

Thanks Temchugh.

(03-30-2014 11:16 AM)temchugh Wrote:  

(03-30-2014 10:51 AM)MemOwl Wrote:  

(03-30-2014 09:53 AM)temchugh Wrote:  The r-squared is low, but this just means that there are a lot of factors in addition to SoS that affect OBP. The low r-squared does not mean that SoS is not important.

what is the t statistic for SOS?

Sure, make me go to the "data analysis" package in Excel.

For SoS as a predictor of OBP, the p-value is almost exactly 0.05 (p = 0.05004 according to Excel). In other words, there is a 5% chance that the apparent relationship between SoS and OBP is due to random chance. If you account for the fact that I predicted a positive relationship (OBP increases with increasing SoS) and observed a positive relationship, the probability drops to 2.5%.

This is taking statistics to the absurd level. You do realize that SoS is a sequential ranking and is NOT reflective of an absolute differential between teams, as OBP, AVG, SLG, ERA and other stats are. In other words, there might be very, very little difference in SoS between the #20 ranked team and the #35 ranked team...and the difference between the #50th ranked team in SoS might not be that much better than the 95th ranked team. To even attempt to calculate a correlation here is a bit ridiculous.

But the point being made is that OBP is affected by quality of opposition, as reflected in SOS. That's not only a reasonable assumption but statistically verifiable. You aren't claiming that pitching and fielding don't differ much between strong teams and weak teams, are you?

(03-30-2014 12:58 PM)waltgreenberg Wrote:  

(03-30-2014 11:16 AM)temchugh Wrote:  

(03-30-2014 10:51 AM)MemOwl Wrote:  

(03-30-2014 09:53 AM)temchugh Wrote:  The r-squared is low, but this just means that there are a lot of factors in addition to SoS that affect OBP. The low r-squared does not mean that SoS is not important.

what is the t statistic for SOS?

Sure, make me go to the "data analysis" package in Excel.

For SoS as a predictor of OBP, the p-value is almost exactly 0.05 (p = 0.05004 according to Excel). In other words, there is a 5% chance that the apparent relationship between SoS and OBP is due to random chance. If you account for the fact that I predicted a positive relationship (OBP increases with increasing SoS) and observed a positive relationship, the probability drops to 2.5%.

This is taking statistics to the absurd level. You do realize that SoS is a sequential ranking and is NOT reflective of an absolute differential between teams, as OBP, AVG, SLG, ERA and other stats are. In other words, there might be very, very little difference in SoS between the #20 ranked team and the #35 ranked team...and the difference between the #50th ranked team in SoS might not be that much better than the 95th ranked team. To even attempt to calculate a correlation here is a bit ridiculous.

It's not ideal, but it's far from absurd. Having Boyd's SoS valuations rather than his ordinal SoS ranking would be better for sure, but it's not useless. Also, you raise an interesting point about the pattern of team quality; it might not be linar. #1 might be as much better than #10 as #10 is better than #100, or something like that. In which case temchugh could just perform regression against log of SoS.

But I think he's already done enough.