Thursday, September 30, 2010

Podcast: SJSU recap & ISU preview with SoCalPat, and interview with Devonte Christopher

This show features San Jose State recap and looking ahead to Iowa State with fan guest SoCalPat and interview with Devonte Christopher.

Listen now by clicking HERE.


Wednesday, September 29, 2010

Utah by the Numbers: Week 5, Post 1


Week 5, Post 1

Today, we take a look at Utah in a few statistical categories after four games into the season.

First off, Utah's offensive line has been phenomenal. I'll be the first to admit that Utah's first few games have not been the toughest. However, sacks are given up to even the worst of teams, so Utah's single sack allowed through four games speaks volumes of the offensive line unit (stat). Additionally, recall that Pittsburgh led the nation in sacks the previous year (stat), boasting two of the nation's premier pass-rushing defensive ends (Romeus, Sheard). For Utah to have only given up 1 sack one third of the way through the season is impressive.

Additionally, Utah has only given up 6 tackles for loss (TFL) in 4 games, a number which leaves them tops in the country through 4 games (stat). This stat also indicates Utah's line is not allowing penetration into the backfield and keeping gaps open long enough for positive yardage gains on every play. This number could dip as the schedule gets tougher, but to be tops in the country after 4 games is a very positive sign, regardless of who Utah has played.


Tackles for Loss Allowed

Rank
Name
Gm
Solo
Ast
Yds
Total
PG
1
4
5
2
19
6.0
1.50
2
4
6
4
47
8.0
2.00
3
4
5
8
31
9.0
2.25
4
3
8
4
28
10.0
3.33
4
3
8
4
36
10.0
3.33
4
3
8
4
67
10.0
3.33
7
4
11
6
36
14.0
3.50

Sacks Allowed

Rank
Name
Gm
Total
Yds
PG
1
4
1.0
4
.25
1
4
1.0
4
.25
1
4
1.0
5
.25
1
4
1.0
6
.25
1
4
1.0
6
.25
1
4
1.0
8
.25
1
4
1.0
8
.25
1
4
1.0
10
.25
1
4
1.0
19
.25


Predictive Statistic (Pass Efficiency Differential Margin)

One statistic which I enjoy measuring for predicting outcomes is the pass efficiency differential margin (PEDM). This is a metric we'll be revisiting every week in the "Utah by the Numbers" posts. This metric is obtained by taking the difference between the offensive and defensive pass efficiency ratings for a team.

Pass efficiency (PE) is a measure of a team's passing ability, which is measured by four categories: (1) yards per pass attempt, (2) pass completions per pass attempt, (3) touchdowns per pass attempt and (4) interceptions per pass attempt. In the NCAA formula, four constants (i.e., 8.4, 100, 330, and 200) are used such that an average passer will have a rating close to 100. Pass efficiency is explained in more detail (here). Pass efficiency defense is calculated using the same formula, except it measures the PE of the opposing quarterback from week to week. Cumulatively, as opposing QB numbers are tallied each week, the pass efficiency defense is determined from the cumulative opposing QB numbers.

The Pass efficiency differential margin (PEDM) is the difference between pass efficiency (PE) and pass efficiency defense (PED), and I believe it is a good predictor for wins and losses.

There are of course many other indicators that can determine the outcome of a football game, such as home/away, turnover margin, weather, special teams play, etc., etc. However, I believe that this metric (PEDM) is particularly suited to predict Utah football outcomes for one reason: Utah's defense has consistently shown it can stop the run over the years. Their defensive system hasn't changed for years under Kyle Whittingham, and it has long been known for imposing defensive lines, filling up the box, and stuffing the run. In other words, I'm not paying much attention to the run defense here, since I'm assuming this is generally a constant under Utah's defensive scheme, and they are not often going to get beat by giving up massive amounts of rushing yardage.

With that disclaimer stated, let's look at the numbers: CLICK HERE

Since I'm using the difference between team's PEDM to predict wins and losses each week, I use the cumulative data from the beginning of the season up to the week before the game to be predicted. For example, to predict the Iowa State vs. Utah game winner (week 6), I'd include all the data for each team prior to that game (weeks 1 - 5).

A few things should be pointed out when using this analysis:

(1) This predictive power of this metric becomes much more reliable as the season wears on. Early on, teams with particulary hard or easy schedules may not reflect an accurate PEDM.
(2) As the conference play begins and teams begin to play the same teams within the conference, the metric becomes much more robust and meaningful.
(3) Obviously, the PEDM metric can be effected suddenly when a QB is injured, or switched. For example, a team with a great QB1, but a terrible QB2 might have a great rating after 5 games. But if QB1 gets injured, the metric might normally predict an easy win, even though QB2 is going to hurt those chances significantly.
(4) As with (3) above, this is another reason why I feel this metric is particularly suited to predict Utah outcomes this year, since we've already seen good levels of production from both QB1 (Wynn) and QB2 (Cain) early in the season.

A quick look at the data reveals some interesting observations:

(1) Pass efficiency differential margins (PEDM) are shown in the colored column for Utah's opponents (left) and Utah (right). The Legend at right shows what the colors mean. Green means you're a good team . . . and red means . . . you're not. Green = good. Red = dead. you get it.

(2) Since no data is available for the first game of the season (PITT), I used the PEDM for both teams from the conclusion of 2009 (post bowl-game). A positive difference of 1.97 is a small positive margin, indicating a narrow win. Utah beat Pitt 27-24 . . . in OT. Check.

(3) UNLV and NM games show PEDM differences of +79.27 and +110.89 in Utah's favor, easily predicting wins, and they were . . . blowouts. The game against SJSU shows a PEDM diff of +91.26 . . . another blowout. Check, Check, and . . . Check.

(4) The ISU game currently shows a +72.22 PEDM difference in Utah's favor, but this number will change after ISU plays its week 5 game against Texas Tech. I'll update the number then, but it's not going to change enough to effect the prediction of a Win! The number indicates that this should be the toughest game since Pittsburgh, but only marginally more difficult than UNLV. In other words . . . not that tough. Given that the game is on the road, it will be slightly more difficult, but this should still be a big win for Utah.

(5) I've shown the PEDM data for each of Utah's opponents up to the current week. I will only update the information for the teams Utah hasn't played, so that the PEDM data reflects what it was the week before the game was played. For example, this week I'll update ISU's data and all teams further out, but will not update any teams data prior to ISU.

(6) Glancing ahead at future teams (with cumulative data through week 4), It looks like the last five teams will be the toughest (as we expect). If early stats are accurate, then it looks like after TCU, the SDSU game might be our toughest challenge. Things should become more clear once a few more rounds of conference games have been played. In the mean time, Utah can feel comfortable with some pretty convincing statistical evidence in their favor . . . at least for the next few games.

Week 5, Post 1