2015 Adjusted Pythagorean Record: Mountain West

Last week, we looked at how Mountain West teams fared in terms of yards per play. This week, we turn our attention to how the season played out in terms of the Adjusted Pythagorean Record, or APR. For an in-depth look at APR, click here. If you didn’t feel like clicking, here is the Reader’s Digest version. APR looks at how well a team scores and prevents touchdowns. Non-offensive touchdowns, field goals, extra points, and safeties are excluded. The ratio of offensive touchdowns to touchdowns allowed is converted into a winning percentage. Pretty simple actually.

Once again, here are the 2015 Mountain West standings.

And here are the APR standings sorted by division with conference rank in offensive touchdowns, touchdowns allowed, and APR in parentheses. This includes conference games only with the championship game excluded.

Finally, Mountain West teams are sorted by the difference between their actual number of wins and their expected number of wins according to APR.

Colorado State was the only Mountain West team to significantly outperform their APR numbers. The Rams actually allowed more touchdowns than they scored, but managed to win five of eight league games (their bowl game with conference rival Nevada is excluded from this analysis). The Rams won both of their one score league games and were outscored by significant margins in each of their league losses. Boise State, San Diego State, and Utah State beat the Rams by a combined 70 points.

Since APR didn’t give us a whole lot to discuss, let’s talk about something else, namely the Mountain West’s standing among mid-major (Group of Five) leagues. With the advent of the College Football Playoff, the current structure of haves (Power Five) and have nots (Group of Five) has been in place for just two seasons. The table below lists how each Group of Five conference, as well as the Group of Five independents (and BYU is considered Group of Five here) have fared against each other (regular season games only) in 2014 and 2015.

After faring well against their fellow mid-majors in 2014, the conference took a step back in 2015, winning just a third of their games against other Group of Five members. This could well be a one-year blip, so let’s look at things another way. This next table lists every current Mountain West team alphabetically and indicates the last season they finished ranked in the AP Poll and the last season during which they appeared in the AP Poll at any point.

Outside of Boise State, the results for the other Mountain West teams are not too great. San Jose State and Utah State each registered historic seasons in 2012, and while they have been contending for bowl games in the three seasons since, those were clearly outlier years. Similarly, Nevada rode Colin Kaepernick to a top-10 finish in 2010, but has not sniffed the polls since. While Fresno State flirted with a BCS bowl bid in 2013, they have not finished a season ranked in over ten years. I could go on. If the Mountain West wants to assert itself as the best mid-major conference it needs a team to join Boise State in the national conscience. San Diego State ran roughshod over the Mountain West in 2015, but the Aztecs stumbled in the non-conference, losing to California and Penn State as well to South Alabama of the Sun Belt. Group of Five teams are at a disadvantage when they go up against Power Five schools, but Mountain West teams can improve their national profile by winning more games in the non-conference against teams of similar regard.

2015 Yards Per Play: Mountain West

Only four more conferences to go in our 2015 recap. This week we go over the Mountain West. Here are the 2015 Mountain West standings.

So we know what each team achieved, but how did they perform? To answer that, here are the Yards Per Play (YPP), Yards Per Play Allowed (YPA) and Net Yards Per Play (Net) numbers for each Mountain West team. This includes conference play only, with the championship game not included. The teams are sorted by division by Net YPP with conference rank in parentheses.

College football teams play either eight or nine conference games. Consequently, their record in such a small sample may not be indicative of their quality of play. A few fortuitous bounces here or there can be the difference between another ho-hum campaign or a special season. Randomness and other factors outside of our perception play a role in determining the standings. It would be fantastic if college football teams played 100 or even 1000 games. Then we could have a better idea about which teams were really the best. Alas, players would miss too much class time, their bodies would be battered beyond recognition, and I would never leave the couch. As it is, we have to make do with the handful of games teams do play. In those games, we can learn a lot from a team’s Yards per Play (YPP). Since 2005, I have collected YPP data for every conference. I use conference games only because teams play such divergent non-conference schedules and the teams within a conference tend to be of similar quality. By running a regression analysis between a team’s Net YPP (the difference between their Yards per Play and Yards per Play Allowed) and their conference winning percentage, we can see if Net YPP is a decent predictor of a team’s record. Spoiler alert. It is. For the statistically inclined, the correlation coefficient between a team’s Net YPP in conference play and their conference record is around .66. Since Net YPP is a solid predictor of a team’s conference record, we can use it to identify which teams had a significant disparity between their conference record as predicted by Net YPP and their actual conference record. I used a difference of .200 between predicted and actual winning percentage as the threshold for ‘significant’. Why .200? It is a little arbitrary, but .200 corresponds to a difference of 1.6 games over an eight game conference schedule and 1.8 games over a nine game one. Over or underperforming by more than a game and a half in a small sample seems significant to me. In the 2015 season, which teams in the Mountain West met this threshold? Here are the Mountain West teams sorted by performance over what would be expected from their Net YPP numbers.

Only one team saw their actual record differ significantly from their expected record based on their YPP differentials. And boy did it ever differ. For the second time in three seasons, Hawaii finished winless in Mountain West play. The 2013 team also posted decent YPP numbers and improved to three league wins in 2014, so there is hope for new coach Nick Rolovich. So how did Hawaii go about losing all their conference games despite bad, but not horrendous YPP numbers? Close games are not the culprit, as Hawaii only lost a single one-score league game (by a single point at New Mexico). No, turnovers told the story for the Warriors. Hawaii turned the ball over 26 times in their eight conference games, while only forcing six of their own. Their in-conference turnover margin of -20 was by far the worst in the conference (13 worse than second to last Wyoming). We’ll discuss more about this historic margin later. I am not making the argument that Hawaii was a ‘good’ team in 2015. They were shut out in each of their first three road games (albeit against strong competition in Ohio State, Wisconsin, and Boise State). However, they did show signs of life early in the year by beating Colorado of the Pac-12 thanks to a little assist from some bumbling officials. If their turnover margin was merely bad and not historically poor, the Warriors probably would have scrounged up at least one and potentially a pair of conference wins.

Hawaii was not the only Mountain West team with a historical in-conference turnover margin in 2015. While Hawaii was struggling with a -20 margin, San Diego State was rolling through the conference en route to an undefeated record and league title with a +19 turnover margin (excluding their championship game win over Air Force). To get a handle on what one might expect from these two polar opposites going forward, I decided to look at teams with similar extreme in-conference turnover margins and see how they performed the following year. Here we’ll define extreme as averaging two more or two less turnovers per game that your opponent. For an eight game conference schedule, that would equate to a turnover margin of +16 or -16. Similarly, for a nine game schedule that would be either +18 or -18. Hawaii and San Diego State were certainly unique in 2015. Only ten other teams since 2005 posted such an extreme turnover margin (Arkansas State also fit the criteria, but like the Warriors and Aztecs, their follow up performance is unknown at this time). We’ll start with the teams with extreme negative turnover margins.

Obviously, teams with extremely poor turnover margins don’t tend to win a lot of games. These four squads combined to go 2-28 in conference play. Perhaps not surprisingly, three of the teams had new head coaches the following year. Oklahoma State elected to retain their head coach (probably since he was in his first season), and that has worked out pretty well for the Cowboys. New Mexico State is a bit of an oddity, as they became a college football independent after their turnover plagued final season in the WAC. That is why there is no follow up conference record listed for them. As for the other three teams, well, they all improved their conference record by at least two games with Oklahoma State and Wyoming qualifying for bowls. For what it’s worth, New Mexico State’s overall record improved from 1-11 to 2-10, so each member of the quartet improved the next season. A pessimist might point out each team had nowhere to go but up after their poor showings and a statistician might highlight the small sample size here. However, another statistician might bring up something about regression (or in this case progression) to the mean and opine that an extremely poor turnover performance is unlikely to be repeated. As for me, I would set the over/under on league wins at two for Hawaii in 2016.

Now let’s look at the other side of the coin. Here are teams with extreme positive turnover margins.

In a not too surprising development, teams with historically great turnover margins tend to have good records. These six teams combined to go 47-3 with Oregon and Toledo being the only teams to not win a conference title. Unlike the poor turnover teams there was continuity at the head coaching position the following season with only Oregon losing their head coach (to the NFL no less). For the most part, each team remained strong the next year with only Kansas State falling out of contention for a conference title. Each team qualified for a bowl game in the following season and two thirds of the teams declined by one game or less. An optimist would likely say these teams had nowhere to go but down after compiling their pristine conference records, and once again, a point can be made about small sample sizes. However, as with Hawaii, San Diego State is unlikely to see their extreme turnover margin repeated. The Aztecs did not throw a single interception in conference play last year! While I expect them to be contenders in the conference in 2016, expecting another undefeated, scorched earth run through the Mountain West is likely folly.

2015 Adjusted Pythagorean Record: MAC

Last week, we looked at how MAC teams fared in terms of yards per play. This week, we turn our attention to how the season played out in terms of the Adjusted Pythagorean Record, or APR. For an in-depth look at APR, click here. If you didn’t feel like clicking, here is the Reader’s Digest version. APR looks at how well a team scores and prevents touchdowns. Non-offensive touchdowns, field goals, extra points, and safeties are excluded. The ratio of offensive touchdowns to touchdowns allowed is converted into a winning percentage. Pretty simple actually.

Once again, here are the 2015 MAC standings.

And here are the APR standings sorted by division with conference rank in offensive touchdowns, touchdowns allowed, and APR in parentheses. This includes conference games only with the championship game excluded.

Finally, MAC teams are sorted by the difference between their actual number of wins and their expected number of wins according to APR.

Kent State was the lone MAC school to see a significant difference between their APR and their actual record. Once you look at their underlying offensive performance, the reason for this disparity is quite simple. Kent State scored six, yes six, offensive touchdowns in their eight conference games. This kind of futility often results in a one or zero win campaign. However, Kent State actually opened MAC play 2-1, by clustering their touchdowns at favorable times and playing decent defense. Despite their 2-1 MAC record, the Golden Flashes had already been outscored by 20 points. Over their final six conference games, only one would be decided by less than 13 points and the Golden Flashes would be outscored by more than 18 points per game.

Despite their historical offensive ineptitude, Kent State fans might have at least a little reason for optimism heading into 2016. The following table lists the other MAC teams that have failed to score more than 10 offensive touchdowns in conference play and their follow up performance the next year.

Based on an admitted small sample size, it appears quite difficult to perform so poorly offensively for two consecutive seasons. Each team that scored 10 or fewer offensive touchdowns rebounded to score at least 23 in their epilogue. Three out of four schools also saw their conference record improve. This is perhaps not too surprising since their offenses returned from the abyss. In the interest of curbing the enthusiasm of Kent State fans, it should be noted that three of the four teams also felt compelled to change coaches after their dreadful offensive showings. Eastern Michigan was the only school to retain their coach, while the other three brought in fresh blood (or old fresh blood) to revitalize their teams. Barring an unforeseen set of circumstances, Kent State will be led by Paul Haynes (don’t worry if you didn’t know who their coach was) for the fourth consecutive year in 2016. We’ll see if he is able to coax a similar offensive improvement out of the Golden Flashes.

2015 Yards Per Play: MAC

Our 2015 conference recaps now take us to the Big 10's little brother, the MAC. Here are the 2015 MAC standings.

So we know what each team achieved, but how did they perform? To answer that, here are the Yards Per Play (YPP), Yards Per Play Allowed (YPA) and Net Yards Per Play (Net) numbers for each MAC team. This includes conference play only, with the championship game not included. The teams are sorted by division by Net YPP with conference rank in parentheses.

College football teams play either eight or nine conference games. Consequently, their record in such a small sample may not be indicative of their quality of play. A few fortuitous bounces here or there can be the difference between another ho-hum campaign or a special season. Randomness and other factors outside of our perception play a role in determining the standings. It would be fantastic if college football teams played 100 or even 1000 games. Then we could have a better idea about which teams were really the best. Alas, players would miss too much class time, their bodies would be battered beyond recognition, and I would never leave the couch. As it is, we have to make do with the handful of games teams do play. In those games, we can learn a lot from a team’s Yards per Play (YPP). Since 2005, I have collected YPP data for every conference. I use conference games only because teams play such divergent non-conference schedules and the teams within a conference tend to be of similar quality. By running a regression analysis between a team’s Net YPP (the difference between their Yards per Play and Yards per Play Allowed) and their conference winning percentage, we can see if Net YPP is a decent predictor of a team’s record. Spoiler alert. It is. For the statistically inclined, the correlation coefficient between a team’s Net YPP in conference play and their conference record is around .66. Since Net YPP is a solid predictor of a team’s conference record, we can use it to identify which teams had a significant disparity between their conference record as predicted by Net YPP and their actual conference record. I used a difference of .200 between predicted and actual winning percentage as the threshold for ‘significant’. Why .200? It is a little arbitrary, but .200 corresponds to a difference of 1.6 games over an eight game conference schedule and 1.8 games over a nine game one. Over or under-performing by more than a game and a half in a small sample seems significant to me. In the 2015 season, which teams in the MAC met this threshold? Here are the MAC teams sorted by performance over what would be expected from their Net YPP numbers.

Only two teams in the MAC met the threshold, with both just barely eclipsing the magic number. Ohio had the statistical profile of a slightly below average MAC team, but managed to win more than half their games and finish second in the MAC East. The Bobcats were 2-0 in one-score league games, but were hardly extremely lucky in that category. No, the most likely explanation for Ohio exceeding their YPP numbers is the fact that they played good, but not great in most of their wins, while they were absolutely destroyed in each of their three losses. Ohio won five games, and while three games by double-digits, their average MAC win was by just over 16 points. Meanwhile, each of their three league losses came by at least 24 points and two were by at least 35 points. On the other side of the coin, Massachusetts, in their MAC swan song, had a better statistical profile than Ohio, but won less than half as many games as the Bobcats. The Minutemen were a little unlucky, going 1-3 in one-score MAC games, but were not significantly unlucky. Whereas Ohio played horrendously in their three losses (being outscored by 97 points), Massachusetts was competitive in almost all their games. The Minutemen dropped their six league games by a total of 66 points. The Minutemen were consistently below average, but probably deserved an extra win or two based on how they played. The Minutemen end their disappointing quadrennial sojourn in the MAC with a 7-25 league record.

Frank Solich is the dean of MAC coaches, having joined the Bobcats prior to the 2005 season. Under his guidance, the Bobcats have experienced great success. They have played in three MAC Championship Games, made seven bowl appearances, and spent time in the top 25 of the AP Poll. However, the one accomplishment that has eluded Solich during his tenure is a MAC title. Here are the cumulative MAC standings since Solich has been in Athens, Ohio.

The Bobcats are tied for fourth overall in MAC winning percentage (and tied for first among teams from the East with Bowling Green) since 2005. However, while the three teams ahead of and tied with them have combined for eight titles, the Bobcats have not been able to break through. Meanwhile, Buffalo, Miami, and Akron have combined to win about a third of their league games since 2005, but own three league championships! As a wise man once said: I’d rather be lucky than good.

In another interesting piece of statistical minutia, Toledo does not even have a MAC Championship Game appearance despite posting the third best league mark since 2005! Part of this is because they play in the stronger MAC West where Northern Illinois has won six consecutive division titles under three different head coaches.

2015 Adjusted Pythagorean Record: Conference USA

Last week, we looked at how Conference USA teams fared in terms of yards per play. This week, we turn our attention to how the season played out in terms of the Adjusted Pythagorean Record, or APR. For an in-depth look at APR, click here. If you didn’t feel like clicking, here is the Reader’s Digest version. APR looks at how well a team scores and prevents touchdowns. Non-offensive touchdowns, field goals, extra points, and safeties are excluded. The ratio of offensive touchdowns to touchdowns allowed is converted into a winning percentage. Pretty simple actually.

Once again, here are the 2015 Conference USA standings.

And here are the APR standings sorted by division with conference rank in offensive touchdowns, touchdowns allowed, and APR in parentheses. This includes conference games only with the championship game excluded.

Finally, the Conference USA teams are sorted by the difference between their actual number of wins and their expected number of wins according to APR.

No team significantly over or under performed relative to their expected record based on APR. That being the case, let's talk about offense in Conference USA.

With Western Kentucky enjoying a phenomenal offensive campaign (and season in general) culminating with their first ever finish in the final AP Poll, I decided to look back at the eleven years of Conference USA YPP and APR data I have collected to determine the best offense Conference USA has seen 2005. The following table lists the top team for each season since 2005 in Conference USA in Yards per Play and Offensive Touchdowns. The actual number of yards per play and touchdowns are also listed. Since Conference USA has played an eight-game league slate for the entire period (2005-2015), there is no need to adjust the touchdowns to a per game basis. I decided to use both metrics as the best offense should be able to move the ball well and pay off drives by scoring touchdowns.

Some teams were able to do one or the other, but the best should be proficient at both. For example, in 2005, UAB, quarterbacked by Darrell Hackney and coached by lesser Mack Brown moved the ball efficiently, but only scored 29 offensive touchdowns in their eight league games (fourth best in the conference). Failing to finish drives is one reason the Blazers managed just a 3-5 conference record despite their moving the ball well. Similarly, SMU in 2010 advanced to the Conference USA Championship Game and led the league in yards per play, but scored just 28 touchdowns (seventh in the league). That being said, the best offense in Conference USA should probably be tops in their respective season by both measures. Western Kentucky in 2015 certainly fits that bill. Led by quarterback Brandon Doughty, the Hilltoppers averaged nearly eight yards per play against league foes and scored more than six touchdowns per game (or more than one and a half per quarter). However, even those phenomenal numbers pale in comparison to the ones posted by Houston in 2011. The Cougars, with future NFL players Case Keenum and Patrick Edwards and coached by Kevin Sumlin averaged more than eight yards per play and scored seven touchdowns per game against Conference USA foes! Plus, the Cougars did this in a stronger league. With Conference USA losing members to the American Athletic Conference and resorting to poaching ersatz schools from the Sun Belt, WAC, and FCS, the Hilltoppers did not face nearly as much resistance from their conference opponents.

2015 Yards Per Play: Conference USA

After six weeks of power conference analysis, we make our triumphant return to the Group of Five. Here are the 2015 Conference USA standings.

So we know what each team achieved, but how did they perform? To answer that, here are the Yards Per Play (YPP), Yards Per Play Allowed (YPA) and Net Yards Per Play (Net) numbers for each Conference USA team. This includes conference play only, with the championship game not included. The teams are sorted by division by Net YPP with conference rank in parentheses.

College football teams play either eight or nine conference games. Consequently, their record in such a small sample may not be indicative of their quality of play. A few fortuitous bounces here or there can be the difference between another ho-hum campaign or a special season. Randomness and other factors outside of our perception play a role in determining the standings. It would be fantastic if college football teams played 100 or even 1000 games. Then we could have a better idea about which teams were really the best. Alas, players would miss too much class time, their bodies would be battered beyond recognition, and I would never leave the couch. As it is, we have to make do with the handful of games teams do play. In those games, we can learn a lot from a team’s Yards per Play (YPP). Since 2005, I have collected YPP data for every conference. I use conference games only because teams play such divergent non-conference schedules and the teams within a conference tend to be of similar quality. By running a regression analysis between a team’s Net YPP (the difference between their Yards per Play and Yards per Play Allowed) and their conference winning percentage, we can see if Net YPP is a decent predictor of a team’s record. Spoiler alert. It is. For the statistically inclined, the correlation coefficient between a team’s Net YPP in conference play and their conference record is around .66. Since Net YPP is a solid predictor of a team’s conference record, we can use it to identify which teams had a significant disparity between their conference record as predicted by Net YPP and their actual conference record. I used a difference of .200 between predicted and actual winning percentage as the threshold for ‘significant’. Why .200? It is a little arbitrary, but .200 corresponds to a difference of 1.6 games over an eight game conference schedule and 1.8 games over a nine game one. Over or under-performing by more than a game and a half in a small sample seems significant to me. In the 2015 season, which teams in Conference USA met this threshold? Here are the Conference USA teams sorted by performance over what would be expected from their Net YPP numbers.

Only one team saw a significant disparity between their expected record based on YPP and their actual record. That team was Rice. The Owls were average on the offensive side of the ball, ranking seventh of thirteen teams. However, defensively, the Owls were a sieve, undeserving of their raptor nickname. The Owls ranked dead last defensively, permitting over seven yards per play (more than a half yard worse than second to last North Texas). They did have the misfortune of taking on the top three offenses in Conference USA (Western Kentucky, Southern Miss, and Louisiana Tech), during which they allowed 156 points. However, they also faced the bottom four offenses (Florida Atlantic, UTSA, UTEP, and Charlotte), so the schedule makers cannot be blamed for their harrowing defensive showing. How did the Owls manage to win three games despite such unflattering peripherals? Unlike most teams that significantly exceed their YPP numbers, close games and turnovers are not the culprit here. The Owls went just 1-1 in one-score league games and actually had a negative in-conference turnover margin. No, the reason for the difference is the fact that the Owls played horribly in their losses and just alright in their wins. In their three wins, they outscored North Texas, Florida Atlantic, and Charlotte (three teams that combined for just five wins against FBS opponents I might add) by 35 points. However, in their five league losses, they were outscored by 132 points. For the Rice Owls, this was certainly not the first time they had drastically exceeded their expected YPP record. In fact, among mid-major (Group of Five) teams since 2005 (the year my YPP numbers go back to), Rice has exceeded their expected record the most.

Over a long sample size (eleven seasons), Rice has exceeded their expected conference record by an average of .186 percentage points per season. For an eight game conference schedule, this works out to nearly a game and a half per season! The man responsible for most of this success is David Bailiff. Over his nine-year tenure, the Owls have exceeded their expected record by about .181 percentage points per season. You may notice this is slightly below their cumulative average of .186. This is thanks to Todd Graham’s one season in charge. In 2006, the Owls were an amazing .452 percentage points ahead of where they would have been expected to finish based on their YPP numbers (thanks to a 5-1 mark in one-score conference games). Graham bolted for Tulsa after the fluky season, and while he has been a decent over-performer at his numerous stops since (exceeding his expected record on average by about .084 percentage points) his successor has toiled in relative obscurity and accomplished quite a bit at a very difficult job. Just for the sake of completeness, I would also like to point out the job Pete Lembo did over five years at Ball State.

He also consistently exceeded pedestrian or worse YPP numbers and produced a pair of bowl teams at Ball State before leaving to become Maryland’s special teams coordinator.

2015 Adjusted Pythagorean Record: Big 12

Last week, we looked at how Big 12 teams fared in terms of yards per play. This week, we turn our attention to how the season played out in terms of the Adjusted Pythagorean Record, or APR. For an in-depth look at APR, click here. If you didn’t feel like clicking, here is the Reader’s Digest version. APR looks at how well a team scores and prevents touchdowns. Non-offensive touchdowns, field goals, extra points, and safeties are excluded. The ratio of offensive touchdowns to touchdowns allowed is converted into a winning percentage. Pretty simple actually.

Once again, here are the 2015 Big 12 standings.

And here are the APR standings sorted by rank with conference rank in offensive touchdowns, touchdowns allowed, and APR in parentheses. This includes conference games only.

Finally, the Big 12 teams are sorted by the difference between their actual number of wins and their expected number of wins according to APR.

Oklahoma State was the only team that saw their actual record differ significantly from their APR. The Cowboys also exceeded their expected record based on Net YPP, so we won't bother examining them again. Instead, lets talk about the bonkers season Texas Tech enjoyed.

A few weeks ago, I unveiled a new stat I called the ‘Excitement Index’. Basically it measured how often offensive touchdowns were scored in a team’s games. Boston College rated as the least exciting team since 2005 by this measure. I also indicated a team from 2015 rated pretty highly. That team played its home games in Lubock, Texas. The Texas Tech Red Raiders and their opponents combined to score an amazing 104 touchdowns in nine conference games. Perhaps no game was more indicative of their season than their back-and-forth 55-52 loss to TCU. The top ten teams since 2005 in the ‘Excitement Index’ are listed below.

Let’s take a moment to celebrate the absurdity of Louisiana Tech’s 2012 season. The Bulldogs, as you may remember, played in the sendoff season for the Western Athletic Conference. The league had just seven teams, including two FBS novices (Texas State and Texas-San Antonio), so there were only six conference games. The Bulldogs and their opponents averaged just over eleven and a half offensive touchdowns in those six contests. Louisiana Tech spent parts of that season in the top 25 before finishing 8-4. Their 8-4 record was not good enough for a bowl bid. This is somewhat ironic considering just three years later there were not enough bowl teams and the NCAA had to use a bullcrap metric to place teams in bowl games. But I digress.

2015 Yards Per Play: Big 12

After dispensing with the Big 10, we shift our attention to the other big conference, the Big 12. Here are the Big 12 standings.

So we know what each team achieved, but how did they perform? To answer that, here are the Yards Per Play (YPP), Yards Per Play Allowed (YPA) and Net Yards Per Play (Net) numbers for each Big 12 team. This includes conference play only. The teams are sorted by Net YPP with conference rank in parentheses.

College football teams play either eight or nine conference games. Consequently, their record in such a small sample may not be indicative of their quality of play. A few fortuitous bounces here or there can be the difference between another ho-hum campaign or a special season. Randomness and other factors outside of our perception play a role in determining the standings. It would be fantastic if college football teams played 100 or even 1000 games. Then we could have a better idea about which teams were really the best. Alas, players would miss too much class time, their bodies would be battered beyond recognition, and I would never leave the couch. As it is, we have to make do with the handful of games teams do play. In those games, we can learn a lot from a team’s Yards per Play (YPP). Since 2005, I have collected YPP data for every conference. I use conference games only because teams play such divergent non-conference schedules and the teams within a conference tend to be of similar quality. By running a regression analysis between a team’s Net YPP (the difference between their Yards per Play and Yards per Play Allowed) and their conference winning percentage, we can see if Net YPP is a decent predictor of a team’s record. Spoiler alert. It is. For the statistically inclined, the correlation coefficient between a team’s Net YPP in conference play and their conference record is around .66. Since Net YPP is a solid predictor of a team’s conference record, we can use it to identify which teams had a significant disparity between their conference record as predicted by Net YPP and their actual conference record. I used a difference of .200 between predicted and actual winning percentage as the threshold for ‘significant’. Why .200? It is a little arbitrary, but .200 corresponds to a difference of 1.6 games over an eight game conference schedule and 1.8 games over a nine game one. Over or under-performing by more than a game and a half in a small sample seems significant to me. In the 2015 season, which teams in the Big 12 met this threshold? Here are the Big 12 teams sorted by performance over what would be expected from their Net YPP numbers.

Only two teams boasted a record that was significantly different than what would have been predicted based on their YPP numbers. Both Oklahoma State and Kansas State exceeded what their record should have been according to the numbers. For Oklahoma State, the reason was simple. The Cowboys were 4-0 in one-score games, beating a quartet of middling outfits (Iowa State, Kansas State, Texas, and West Virginia) by a combined 16 points. Three of those games did come on the road, so give them credit for winning away from Stillwater, but the Cowboys did not have the bona fides of an elite team. On the other hand, Kansas State is a different story. Statistically, the Wildcats were the second worst team in the Big 12 by a significant margin. Their offense was only better than that of their in-state brethren and they made up for it by also having one of the worst defenses in the Big 12. So how did a team that was consistently outplayed on a down to down basis by a significant margin manage to qualify for a bowl games? The Wildcats actually sported a slightly below average 2-3 record in one-score Big 12 games, so we can’t attribute the difference to their record in close games. We’ll have to look elsewhere. One area where Kansas State gained an edge was special teams. The Wildcats returned a punt and three kickoffs for touchdowns in their Big 12 games. Those scores provided the margin of victory in a three point win over Iowa State and a one point win over West Virginia. Another way that Kansas State was able to remain competitive despite their poor overall play was their slow pace.

Based on the plays they ran and the plays their defense faced, the Wildcats saw the least ‘action’ of any Big 12 team in conference play. The Wildcats saw about twelve fewer plays per game than the average Big 12 team and 25 fewer than Texas Tech, the Big 12 leader in that category. When your offense and defense are both inefficient, shortening the game can pay dividends. Finally, Kansas State benefited from their home schedule. Two of their three league wins came at home (with their only road win coming against perennial punching bag Kansas) and while their loss to Oklahoma was not competitive, the Wildcats lost by just seven points each to TCU and Baylor in Manhattan.

One of the major storylines in college football since 2010 has been realignment. Every FBS conference has seen some kind of membership change since the end of the 2010 season. In fact, some conferences have ceased to exist entirely. The Big 12 has been one of the more interesting cases, as three other power conferences raided it. The Big 12 in turn plundered the Big East and Mountain West to steady its membership. When the dust had cleared, the Big 12 lost four teams and added two bringing the total membership to ten teams. The marquee programs in the conference post-realignment are of course, Oklahoma and Texas. While the Sooners have pulled their weight in the conference, and on the national level, Texas has struggled. The Longhorns have endured a pair of losing seasons and have not won the conference since 2009. In the midst of the struggles by the Longhorns, a quartet of non-traditional powers has emerged to ensure the Big 12 remains a player on the national stage.

Between 1980 and 2011, Texas and Oklahoma combined for three national titles, 19 conference titles (in the Big 8, Southwest, and Big 12 conferences), 21 top-ten finishes in the AP Poll, and 41 top-25 AP Poll finishes. Those are pretty good numbers. In that same time span, Baylor, Kansas State, Oklahoma State, and TCU combined for zero national titles, 12 conference titles (with most coming courtesy of TCU in mid-major leagues), nine top-ten finishes in the AP Poll, and 31 top-25 AP Poll finishes. The table below summarizes what I just typed.

However, since 2012, Baylor, Kansas State, Oklahoma State, and TCU have been torchbearers for the Big 12. Oklahoma and Texas have combined for two conference titles, three top-ten finishes in the AP Poll, and four top-25 AP Poll finishes. Oklahoma has contributed all the conference titles, all three of the top-ten finishes, and three of the four top-25 finishes. Meanwhile, Baylor, Kansas State, Oklahoma State, and TCU have combined for four conference titles (with only Oklahoma State failing to register at least a shared title), three top-ten finishes in the AP Poll, and ten top-25 AP Poll finishes. Again, take a gander at the table that summarizes these results.

If we look at just Big 12 performance, the results are even more revealing.

While Oklahoma has the best Big 12 record in that span, Baylor is a close second with Kansas State and Oklahoma State tied for third. TCU is tied with Texas for fifth, but the Horned Frogs have more dominant of late, having gone 15-3 the past two seasons after a 6-12 start to major conference life.

What does all this mean for the Big 12? The good news is that teams have risen while Texas has fallen. While Oklahoma remains the bell cow for the conference, other teams have popped up intermittently to keep the Big 12 on the national radar. The bad news is that these teams may not have staying power. Baylor was an abject dumpster fire until Art Briles got there. They had some moderate success in the 80s and early 90s, but since the Big 12 started, they were the weakest link. The infrastructure has improved, but how far will they fall once Briles leaves? Similarly, Kansas State may have been the worst FBS program when Bill Snyder arrived in the late 80s. During his brief retirement, the Wildcats were middling at best. Snyder will be vacating Manhattan much sooner rather than later. What will the Wildats do when he is gone (for good this time)? Oklahoma State had some good teams under Pat Jones in the 80s (and briefly Les Miles), but Mike Gundy has raised the program to new heights. With that T. Boone Pickens money, the Cowboys may be well positioned for success when Gundy leaves, but it is far from guaranteed. Finally, TCU has exceeded their historical levels under coach Gary Patterson. He has been in Fort Worth for a decade and a half and seen the Horned Frogs go from mid-major power to Big 12 contender. How will this program fare when he leaves? Success of upstarts has played a key role in keeping the Big 12 relevant in the national picture during uncertain times. However, relying on these upstarts to remain prosperous after their regimes change may not be prudent. Perhaps the best case scenario for the Big 12 is a return to glory for another old money program, Texas.

2015 Adjusted Pythagorean Record: Big 10

Last week, we looked at how Big 10 teams fared in terms of yards per play. This week, we turn our attention to how the season played out in terms of the Adjusted Pythagorean Record, or APR. For an in-depth look at APR, click here. If you didn’t feel like clicking, here is the Reader’s Digest version. APR looks at how well a team scores and prevents touchdowns. Non-offensive touchdowns, field goals, extra points, and safeties are excluded. The ratio of offensive touchdowns to touchdowns allowed is converted into a winning percentage. Pretty simple actually.

Once again, here are the 2015 Big 10 standings.

And here are the APR standings sorted by division with conference rank in offensive touchdowns, touchdowns allowed, and APR in parentheses. This includes conference games only with the championship game excluded.

Finally, the Big 10 teams are sorted by the difference between their actual number of wins and their expected number of wins according to APR.

Northwestern was the only Big 10 team with a record substantially different than their expected record based on APR. There was also a positive disconnect between the Wildcats record and their expected record based on YPP. We covered that last week, so unlike Marco Rubio, we won't repeat ourselves here. Instead, we'll take a closer look at Paul Chryst and his first Wisconsin team.

Last week I introduced a new throwaway metric to measure the impact of a coach changing jobs at the FBS level. Since Paul Chryst was the reason I researched the issue, I decided to name the metric after him. Now this week, I want to take a look at how the Badgers performed offensively under Chryst in his first season at the helm. I really have no personal vendetta against Paul Chryst. These are merely observations.

I have touchdown and yards per play data (in conference play) for every FBS team going back to 2005. The chart below lists the offensive touchdowns scored and yards per play averaged by the Badgers in Big 10 play since 2005 with their rank in the conference in parentheses. For easy reference, the chart is color coded based on who was coaching the team. Four different gentlemen have guided the Badgers on the gridiron during this time span: Barry Alvarez (2005), Bret Bielema (2006-2012), Gary Andersen (2013-2014), and Paul Chryst (2015).

Obviously, Chryst’s struggles stand out like a sore thumb. The Badgers endured their lowest touchdown and yards per play output under his watch. Not only, were their raw numbers the lowest in the period examined, but their place among the other Big 10 teams was also at its nadir. Sure, the Badgers suffered injuries in 2015, but their precipitous decline from their historical baselines and particularly from the prior season (when they were second in touchdowns and first in yards per play) has to be at least a little disturbing for Badger fans. Chryst was the team’s offensive coordinator from 2006-2011, when they were running roughshod over the rest of the Big 10 with their power running game and solid, but unspectacular quarterback play (with the exception of their one-year rental of Russell Wilson), so maybe things will turn around. Or maybe Chryst is in over his head as a head coach as could be inferred from his wholly mediocre three seasons at Pitt. In all likelihood, we will get to find out.

I’ll close with a little more statistical minutia regarding the impotence of the Wisconsin offense in 2015. For the first time since 2004, Wisconsin failed to have a single back top 1000 yards rushing. Dare Ogunbowale led the Badgers with 819 yards on the ground in 2015. This total would have ranked behind the second leading rusher for Wisconsin teams in 2014, 2013, 2010, and 2008 (and just 13 yards more than the second leading rushing in 2012). From 2005-2014, six Badgers rushed for over 1000 yards in a season. These six backs (Brian Calhoun, PJ Hill, John Clay, Montee Ball, James White, and Melvin Gordon) combined for twelve 1000 yard seasons. Four of those backs, Calhoun, Ball, White, and Gordon, were drafted by NFL teams.

2015 Yards Per Play: Big 10

Two conference reviews down, eight to go. We move on to the B's now. Here are the Big 10 standings.

So we know what each team achieved, but how did they perform? To answer that, here are the Yards Per Play (YPP), Yards Per Play Allowed (YPA) and Net Yards Per Play (Net) numbers for each Big 10 team. This includes conference play only, with the championship game not included. The teams are sorted by division by Net YPP with conference rank in parentheses.

College football teams play either eight or nine conference games. Consequently, their record in such a small sample may not be indicative of their quality of play. A few fortuitous bounces here or there can be the difference between another ho-hum campaign or a special season. Randomness and other factors outside of our perception play a role in determining the standings. It would be fantastic if college football teams played 100 or even 1000 games. Then we could have a better idea about which teams were really the best. Alas, players would miss too much class time, their bodies would be battered beyond recognition, and I would never leave the couch. As it is, we have to make do with the handful of games teams do play. In those games, we can learn a lot from a team’s Yards per Play (YPP). Since 2005, I have collected YPP data for every conference. I use conference games only because teams play such divergent non-conference schedules and the teams within a conference tend to be of similar quality. By running a regression analysis between a team’s Net YPP (the difference between their Yards per Play and Yards per Play Allowed) and their conference winning percentage, we can see if Net YPP is a decent predictor of a team’s record. Spoiler alert. It is. For the statistically inclined, the correlation coefficient between a team’s Net YPP in conference play and their conference record is around .66. Since Net YPP is a solid predictor of a team’s conference record, we can use it to identify which teams had a significant disparity between their conference record as predicted by Net YPP and their actual conference record. I used a difference of .200 between predicted and actual winning percentage as the threshold for ‘significant’. Why .200? It is a little arbitrary, but .200 corresponds to a difference of 1.6 games over an eight game conference schedule and 1.8 games over a nine game one. Over or under-performing by more than a game and a half in a small sample seems significant to me.In the 2015 season, which teams in the Big 10 met this threshold? Here are the Big 10 teams sorted by performance over what would be expected from their Net YPP numbers.

The Big 10 saw a large number of teams (six) teams finish with records that did not match their YPP numbers. And let’s deal with the elephant in the room. Yes, the numbers say Penn State was the second best team in the league. If you look closely though, you will see that outside of Ohio State, there were no dominant teams in the Big 10 this year. However, some did post dominant records. More on that in a moment. Illinois, Minnesota, and Maryland under-performed based on their YPP numbers while Iowa, Northwestern, and Michigan State produced better records than one would expect. Illinois began the year with an interim head coach after allegations of player abuse cost Tim Beckman his job just before the season started. The Illini cannot blame close losses for the disparity between their record and their expected record. The Illini actually won their only close conference game, edging Nebraska 14-13. Despite finishing with a 5-7 record, Illinois elected to retain coach Bill Cubit. Not all were pleased with this decision. Like Illinois, Minnesota also ended the year with an interim coach, and they too decided to keep him on despite a losing record. Jerry Kill’s health issues resurfaced in 2015 and his abrupt retirement meant Tracy Claeys was now in charge. The Gophers lost a pair of tight games to good teams (Michigan and Iowa) en route to their 2-6 conference finish and were marginally competitive against both Ohio State and Wisconsin. Maryland, like the Gophers and Illini (sensing a theme here?) also ended the year with an interim coach. Randy Edsall was fired after a 2-4 start and disgraced former New Mexico head coach Mike Locksley replaced him. Locksley guided the Terrapins to just one win in their final six games, but that was half as many as he had in nearly five times as many games in the Land of Enchantment. And he avoided a sexual harassment scandal to boot. Maryland was more competitive under Locksley, losing one-score games to both Penn State and Wisconsin under his guidance. For the triumvirate of teams that exceeded their YPP numbers, close games told the story. Iowa, Northwestern, and Michigan State finished a combined 12-1 in one-score conference games with the only loss coming in controversial fashion by the Spartans. Iowa also posted a +13 turnover margin in Big 10 plays (tops in the conference). Iowa, Northwestern, and Michigan State produced gaudy regular season records, but in their bowl games, they were beaten by a combined score of 128-22, providing further ammunition for the argument that they were not quite as good as their record indicated.

Now, I am going to throw some shade toward Mr. Paul Chryst.

Around midseason when Pitt began to look like a contender in the ACC Coastal Division, it looked like they had made a coaching upgrade when their former head coach, Paul Chryst, took the Wisconsin job. Obviously, except in extremely rare instances, one season does not serve as the final evaluation in the success or failure of a head coach. Still, I thought it would be interesting to look at coaches who change jobs and see how both their former and current teams performed with them at and not at the helm. I decided to call my little throwaway metric ‘The Chryst Index’ or TCI. Basically what TCI measures is how much worse the coach’s old team got when he left combined with how much better his new team got when he arrived. Here is a quick rundown on how it is calculated.

1. For starters, TCI can only be measured for coaches who move from one FBS job to another.
2. Start with the coach’s final season at his old job. Subtract the final regular season win total of this season from the final regular season win total under the new coach.
3. Next, move on to the coach’s first season at his new job. Take the final regular season win total of this season and subtract the final regular season win total of the previous season (the last under the previous coach).
4. Subtract the value from step 2 from the value in step 3. This is the TCI number for the coach. As in most things, more is better.

I know that might be a little confusing, but here is how the math plays out for the eponymous Chryst in 2015. His last Pitt team went 6-6 in 2014. Pitt improved to 8-4 in their first season without him. Subtracting 6 from 8 gives us 2. Pitt improved by two games without Chryst, which reflects negatively on him. His first team at Wisconsin went 9-3. Wisconsin went 10-2 in the regular season before Chryst’s arrival. 9-10 gives us -1. Wisconsin declined by one game when Cyryst arrived. Again, this reflects negatively on him. When we subtract the previous value (2) from -1, we get -3. Only four coaches could be evaluated by TCI for the 2015 season. They are listed below.

In leading Florida to the SEC East crown, Jim McElwain was the only FBS coach to change jobs who had a net positive impact on his teams, both new and old. Chryst ranks last among the quartet of coaches who changed jobs in 2015, but his TCI of -3 is far from the worst of the last decade. Before we get to those esteemed gentlemen, let’s look at those coaches who produced the highest TCI since 2006.

Aside from Gus Malzahn, who improved Auburn by an amazing 8 games, most of these coaches benefited from the teams they left careening off a cliff. Some of this is probably a little by design. Hoke, Fedora, Sumlin, and Kelly had all been at their respective schools for at least three years and had been building for this season that achieved a level of success that was out of place for the school’s historical standards. Not only did they lose the coach, but they often lost a lot of really good players from those teams. As for whether a high TCI is a portent of future success, well that is a mixed bag. Brian Kelly has to be considered a success at Notre Dame and Hoke was certainly successful at San Diego State, but Malzahn and Sumlin will enter 2016 on pretty warm seats. Before winning the ACC Coastal in 2015, Fedora was also feeling the heat in Chapel Hill. Now on to the coaches who produced the worst TCI numbers since 2006. Alas, Chryst does not quite make the cut.

Aside from Dan Hawkins, who saw Boise go from a cute mid-major to a burgeoning national power upon his departure, no other coach on this list saw his former team drastically improve. No, they ‘earned’ their position because their new teams struggled. Unlike a high TCI, a low TCI seems to herald trouble for a new coach. Dan Hawkins stuck around Colorado for parts of five seasons, but guided the Buffaloes to only one bowl game and zero winning seasons. Steve Kragthorpe lasted three years at Louisville and produced no winning seasons. Tim Beckman almost made it to his fourth season at Illinois, but also failed to produce a winning season. Dave Doeren has been moderately successful since his disastrous initial campaign at NC State, but the Wolfpack are just 2-16 against ACC teams not located in Winston-Salem or Syracuse. Skip Holtz is by far the biggest success story, rebounding from a poor first season to post back-to-back nine win campaigns at Louisiana Tech.

TCI is not the final word on rating a new football coach, but it can be a useful, if flawed, tool to examine how a coach performed in his first season.

Next week, the Big 10 gets the APR treatment, and we'll take a closer look at Chryst's first Wisconsin team.

2015 Adjusted Pythagorean Record: ACC

Last week, we looked at how ACC teams fared in terms of yards per play. This week, we turn our attention to how the season played out in terms of the Adjusted Pythagorean Record, or APR. For an in-depth look at APR, click here. If you didn’t feel like clicking, here is the Reader’s Digest version. APR looks at how well a team scores and prevents touchdowns. Non-offensive touchdowns, field goals, extra points, and safeties are excluded. The ratio of offensive touchdowns to touchdowns allowed is converted into a winning percentage. Pretty simple actually.

Once again, here are the 2015 ACC standings.

And here are the APR standings sorted by division with conference rank in offensive touchdowns, touchdowns allowed, and APR in parentheses. This includes conference games only with the championship game excluded.

Finally, the ACC teams are sorted by the difference between their actual number of wins and their expected number of wins according to APR.

The ACC saw four teams with significant differences between their APR and actual record. Three of those teams (Boston College, Georgia Tech, and Miami) also saw large differences between their actual record and their record as predicted by their YPP differential. We discussed those three teams last week, so we won’t rehash it here. The other team with a significant difference between their APR and actual record was Duke. The Blue Devils did not have a phenomenal record in close games (3-2) to pump up their place in the standings, but they did have a few blowout losses (35 points to North Carolina and 18 points to Pitt) that tramped down their APR. In fact, the margin in their 35 point loss to the Tar Heels was more than the margin in their four conference victories (24 total points).

Segue.

If you watched any Boston College football games this season, you may have noticed the Eagles didn’t put a lot of points on the board. Yes, they had a harder time scoring than a pimply hunchback at a Kappa Kappa Gamma mixer (thanks, I’m here all weekend). Seriously, though, their offense may have been the worst in college football. However, despite their offensive struggles, the Eagles were competitive in most of their games thanks to a strong defense. This combination of ineptitude and competence got me thinking and in my thinking, I created something I deemed the ‘Excitement Index’. The concept is pretty simple. Most people watch football for the scoring, more specifically, the touchdowns (no one likes field goals). Maybe you are a football snob and you enjoy watching pulling guards smash into linebackers, but I would argue most fans are not that nuanced (or sober). No, they want to see touchdowns. I have APR data for each FBS conference going back to 2005, so I looked at the total number of offensive touchdowns scored and allowed for each team in every conference game dating back more than a decade. I simply added up the offensive touchdowns each team scored with the touchdowns they allowed and divided by the number of conference games played. Why did I use conference games? Well, I have that data readily available. So how does Boston College rate in the ‘Excitement Index’? They are the least exciting team since at least 2005. The average Boston College game in 2015 saw just two and a half combined offensive touchdowns. Boston College as well as the other least exciting teams since 2005 are listed below.

Let me be clear, this is not a slight at Boston College. For my money, their 3-0 ‘boring’ loss to my alma mater (Wake Forest) was a very exciting game. However, I doubt many folks who are not fans of either team switched over to that game. Had the score been something like 70-66, it might have garnered the attention of a few more casual fans. So Boston College ranked dead last of all teams since 2005 in the ‘Excitement Index’. Did any team from 2015 rank first? No. But one team did rank second all time in the measure. You’ll have to wait until we get to that conference before I divulge their identity. That’s what we in the business call a tease. I’ll give you a (probably not needed) hint. They play in the Big 12.