I hope you’ll join me at the new site, where you’ll have access to a plethora of non-stat related reports and opinions on Hoos sports. All my old posts will also make the switch. Look for my first posts soon as part of the highly anticipated Midnight Madness.

1. ^[1] Tony Bennett’s worst nightmare ↩
2. ^[2] after being offered a very lucrative transfer package (not really) ↩

Last week’s loss to Duke was one of the more devastating I’ve seen. The halftime lead evaporated as the previously unstoppable offense suddenly couldn’t muster a first down.^[3] Even past employees and physical structures associated with the football team felt the ripples of this monumentally awfully loss; Al Groh was fired from his defensive coordinator job at Georgia Tech, and the football practice facility roof literally caught on fire. This post will grapple with the uncertain state of the 2012 season following the devastating loss to Duke: are the Hoos “just” in serious danger, or are they already sinking?^[4]

{Hoos 2012 Performance Margin}

I’ll attempt to evaluate the trend in football performances by standardizing the Hoos FBS opponents. The goal is to discern whether, from week to week, the Hoos are performing worse against this statistically standardized opponent. Are the Hoos in the midst of a steady decline, evidencing minimal hope of a rebound, or do they just have a couple miserable performances amongst games against some strong FCS teams?

To standardize the opponents, I began by comparing each opponent’s current rank to the Hoos current Sagarin rank of 89 in the chart below. I then averaged the rank differences and final point differences in the games against each of these teams.^[5] The listed gain is the ratio of average rank difference to average actual point difference.^[6] The concept is, by multiplying the actual point difference by this gain,^[7] then adding it to the rank difference, the average resulting value, listed as the Performance Margin,^[8] should be zero.

To illustrate the concept, consider game 4 against TCU.^[9] TCU is ranked 70 spots ahead of the Hoos (+70). The Hoos lost by 20, which when multiplied by the gain, gives -49.6. Adding this to the rank difference of +70 gives an overall performance margin of 20.4, which is good because its positive. This effectively means that the Hoos outperformed their rank in this game. An average performance of a team with the same rank deficit would result in a PM of 0; corresponding to a 28ish point loss.^[10] This calculation method allows a comparison of losses to losses over the course of the season.

The chart shows the wide range of Performance Margins for the Hoos in 2012, which are plotted in the graph below. An ideal graph would show consistent performances that only vary slightly around zero. The worst case scenario would be a steadily decreasing line that starts with a high positive and ends with a low negative, which would show that the Hoos are getting worse with each passing game. Using our TCU example, a steadily decreasing line would indicate that, if the Hoos were to somehow play TCU every week, they would lose by larger and larger margins in each successive week.

The graph reveals some interesting aspects of the Hoos performances. The two ACC losses are by far the worst games of the season, and the Georgia Tech loss is somewhat worse than the loss to Duke. As expected, the win against Penn State is the best performance, but the loss to LA Tech isn’t that far behind, and was a “better” loss than the one at TCU. Also note that the two highest points are the two home games.^[11]

Overall, the Hoos do not appear to be taking on water and sinking into the Atlantic, as they played competitive games against very strong TCU and LA Tech teams after defeating a rising Penn State team. The ACC performances, however, leave much to be desired. A return to good ‘ol Scott Stadium for the next two ACC games could be exactly what the Hoos need to right the ship. Despite the current state of the team, I would be very, very surprised if the Hoos lost both the Maryland and Wake Forest games.^[12] A small winning streak and a return to .500 in both overall and ACC records is not entirely out of the question.

{Epilogue: Extremely Abbreviated Maryland Preview}

My method of extrapolating Sagarin rankings to predict the final score has been the more accurate of my two attempts at final margin predictions.^[13] Maryland’s rank of 72, with the Hoos way down at 89, predicts an 8 point win for the Terps. Since we just finished discussing the Hoos stronger home field performances, I’ll add the customary 3 points in favor of the home team to the final margin. My final prediction is: Maryland 30 – 25 Hoos.^[14]

1. ^[1] forever immortalized by a decent movie and the cheesiest Celine Dion song ever written (http://youtu.be/zmbw8OycJrE <– you’re welcome), and that’s saying a lot ↩
2. ^[2] I’ll admit, the Hoos football team was never really Titanic-like ↩
3. ^[3] still bothered by the fact that, when asked about what halftime adjustments Duke made, London shrugged it off as the difficulties of playing from behind. I hope he realizes that Duke did in fact make a number of strategic adjustments, and that he can prevent that same adjustment from burning the Hoos again. ↩
4. ^[4] with a quick Maryland preview at the end ↩
5. ^[5] under the grid portion ↩
6. ^[6] well, the absolute value of the actual point difference ↩
7. ^[7] to set it at the same magnitude level as the rank difference …. hopefully this is making sense ↩
8. ^[8] PM in the chart ↩
9. ^[9] still referring to the chart ↩
10. ^[10] make sense now? ↩
11. ^[11] and the Hoos are returning for two straight home games ↩
12. ^[12] with Wake being the more likely win ↩
13. ^[13] if you haven’t seen this method, click on (Football > Game Predictions), it’ll be in any of those posts ↩
14. ^[14] Vegas has the Hoos by 1.5 ↩

The less informed of us might laugh and arrogantly wave our hands in our patterned orange pants and v-sabre bowties^[4] and say “Duke has long been the laughingstock of ACC football, it’s the perfect opportunity for the Hoos to right the ship down in Durham.”^[5] A sobering look at recent seasons reveals that the Hoos have lost three of their last four games against the Blue Devils. ^[6] I’m now holding my breath even more.

This post will attempt to predict the outcome of Saturday’s game.^[7] As per usual, I’m going to slightly alter the method this week. I used two separate methods in predicting the TCU game, but was shortsighted enough to apply my own discussion of intangibles to the resulting range of possible scores; simply averaging the two scores would have predicted an almost exactly correct final margin. So that’s what I’ll do this time. The final prediction follows the descriptions^[8] and individual predictions^[9] for each of the two methods.

{Prediction Method 1: Overall Rank Difference vs. Point Difference and Points Scored}

The first method calculates the overall rank difference, in Sagarin rankings, for the two teams involved in the game and plots this value against the final point margin and points scored. The idea is that any rank difference between two teams can then suggest a final score difference and a point total from the perspective of one team. In this iteration, I limited the games creating this plot to Hoos and Duke games this season.

The above games created the below plot:

The Sagarin rankings have Duke ranked at 86 and the Hoos at 85, thus predicting the Hoos will score 28 points^[10] and win by 1.^[11] Method 1 predicts: Hoos 28 – 27 Duke. As mentioned above, I wanted to verify this score by comparing it to a completely separate final prediction …

{Prediction Method 2: Offensive and Defensive Rank Differences vs. Points Scored}

Football Outsiders separately ranks both offenses and defenses in the FBS. For this method, I plotted the rank difference between Team A’s offense and Team B’s defense versus the number of points that Team A scored in the game. This also works in reverse to create a whole separate set of data points; the difference between Team B’s offense and Team A’s defense and the points scored by Team B.^[12] The following chart shows this data for the same set of games:

This method gives a graph and equation of:

The Duke offense is ranked 72nd, while the Hoos defense is ranked 46th.^[13] Based on these rankings, the above equation predicts 24 points for Duke. On the other side of the ball, the Hoos offense is ranked 78th, while the Duke defense is ranked 105th;^[14] predicting that the Hoos will score 43 points^[15] against Duke. Method 2 gives Hoos 43 – 24 Duke.

{Conclusion}

The Vegas line currently has the Blue Devils winning by 1.5. My two methods produced drastically different results, but at least both picked the “under” on the final score margin. I’ll stick with my averaging method despite the large variation and hope that the errors in these two methods perfectly offset each other.

Final Prediction: Hoos 35 – 26 Duke^[16]

1. ^[1] the title might be a bit of a stretch for Rocco, but he was at least considered good … for a while ↩
2. ^[2] mandatory note that Duke has largely played really terrible teams to amass this impressive record ↩
3. ^[3] although the rest of the ACC isn’t that great either, so who knows. Also, “any given Saturday” etc. etc. ↩
4. ^[4] as you can see, I’ll be commenting on the sports knowledge of a terrible UVA caricature advanced by other members of the ACC. A person who is undoubtedly not you. ↩
5. ^[5] since this caricature would also use nautical metaphors. ↩
6. ^[6] Sad. So very sad. ↩
7. ^[7] My success in predicting previous games can be found here. ↩
8. ^[8] which are worth skipping if you’ve read the descriptions before ↩
9. ^[9] probably shouldn’t skip these ↩
10. ^[10] per the orange line ↩
11. ^[11] the blue line. ↩
12. ^[12] so even though the third column says “D rank difference,” it’s still from the perspective of the opposing team’s offense. ↩
13. ^[13] interestingly, it’s gone up since the TCU game ↩
14. ^[14] yikes. Hopefully a field day for Sims “the savior of the offense” ™ ↩
15. ^[15] oh snap! ↩
16. ^[16] note that any discrepancies from averaging are due to fractions of points that are calculated but not shown in this post ↩

Boise State was the first to make some in-roads into the major conference BCS scene. Others, like the Hoos’ opponent this weekend, have followed. Unfortunately, the Hoos might find out that, while bracket busting is great in March, feisty mid-majors are significantly less fun when it’s your 2-2 season on the bubble.

The Hoos made a decent showing deep in the heart of Texas last weekend. The defense hung tough, while the offense repeatedly shot themselves in their collective feet. Take away a few ill-timed turnovers, a pass that hit Darius Jennings … in the face, and an unnecessary interception-negating penalty on Brandon Phelps, and the Hoos might actually have made a game of it.^[3] Small moral victories via 20 point loss aside, the Hoos face another tough opponent this weekend.

Louisiana Tech, soon to move from the Western Athletic Conference to Conference USA, is the only football team in the FBS to have scored over 50 points in every game this season. The Bulldogs recently demolished fellow 2-2 major conference team Illinois to the tune of 52-24. Although their offense has received much of the attention, they forced six^[4] turnovers against the Fighting Illini. I’m afraid.

This post will attempt to determine if my fears are well-founded through a prediction of Saturday’s game. I’ll stick with my originally successful^[5] method of overall rank difference versus point difference and points scored.^[6] This time, though, I’ll just use the resulting score in lieu of imposing some unfounded discussion of intangibles, which prevented my close prediction from becoming very close last weekend.^[7]

{Overall Rank Difference vs. Point Difference and Points Scored}

The first method calculates the overall rank difference, in Sagarin rankings, for the two teams involved in the game and plots this value against the final point margin and points scored. The idea is that any Sagarin rank difference between two teams can then suggest a final score difference and a point total from the perspective of one team. In this iteration, I limited the games in the sample to the Hoos, LA Tech, and the teams each of these teams have played so far this season.^[8]

Limiting the plotted games to the involved teams and other closely related outcomes hopefully better captures any trends that LA Tech and the Hoos might have with respect to the Sagarin rankings. The above games created the below plot:

Sagarin ranks the Hoos at 74 and Louisiana Tech at 49. Using this rank difference of 25 in the orange equation for Hoos points and the blue equation for final score margin yields:

{Final Prediction}

Louisiana Tech 33 – 23 Hoos

1. ^[1] unless the upset in question directly prevents you from winning a lot of money, then it’s not so much fun ↩
2. ^[2] very proud that I found a way to work this in during football season ↩
3. ^[3] Ifs, ands, and buts, etc., etc. ↩
4. ^[4] 6! ↩
5. ^[5] in the Penn State game ↩
6. ^[6] explanation below for those who are not familiar ↩
7. ^[7] well, that and TCUs decision to go for it and score a TD on fourth down late in the 4th quarter with a 13 point lead ↩
8. ^[8] avoiding double-counting the games in which the listed teams played each others ↩

The Hoos were beaten like a bad FCS team last weekend in a game against an above average conference opponent.^[2] The defense surrendered nearly 400 yards …. in the first half.^[3] It was a disheartening performance after a strong 2-0 start to the season. But more deeply concerning, it raises serious questions about the reasonable expectations for this season moving forward. The Hoos achieved unexpected success last season and are poised to continue their upward swing with the vast majority of the starters accompanying big recruits next season. A performance dip in 2012 would potentially sour some mercurial Hoos fans expecting steady improvement, and at least disappoint those of us with more perspective.^[4]

I should have seen the blowout coming. A young, very inexperienced defense coupled with an exotic offensive attack requiring extensive planning and discipline is a perfect recipe for the resulting abject disaster. But I didn’t see it, and my prediction was way off. As my interminable season preview proved, I believed the defense would hold back the Hoos, but would not prevent them from at least sustaining their success of last season. This week, I’ll look to bounce back just like the downtrodden defense.

On Saturday the Hoos face the #16/17 ranked TCU Horned Frogs in Texas. The goal for this game is to rebound and hang with the Horned Frogs; to prove that the Hoos belong on the same field as a ranked opponent. Another soul-crushing blowout could portend a very long bowl-less season that drags through the widespread mediocrity of the ACC. A close tough-fought game could mean a brighter 2012 closely resembling the successful 2011 season. Although the Hoos will likely lose, the outcome is still critically important. As usual, this post will^[5] predict the outcome of the game. I’ll compare two separate methods of calculating the final score in hopes that combining them will improve the accuracy of the final prediction. You’ll recognize the first method. The second is a new variation on the same theme.

{Prediction Method 1: Overall Rank Difference vs. Point Difference and Points Scored}

The first method calculates the overall rank difference, in Sagarin rankings, for the two teams involved in the game and plots this value against the final point margin and points scored. The idea is that any rank difference between two teams can then suggest a final score difference and a point total from the perspective of one team. In this iteration, I limited the games creating this plot to the Hoos, TCU, and the teams each of these teams have played so far this season.^[6]

Limiting the plotted games to the involved teams and other closely related outcomes hopefully better captures any trends that TCU and the Hoos might have with respect to the Sagarin rankings. The above games created the below plot:

The Sagarin rankings have TCU ranked at 13 and the Hoos at 76, thus predicting the Hoos will score 10 points^[7] and lose by 24.^[8] Method 1 predicts: TCU 34 – 10 Hoos. As mentioned above, I wanted to verify this score by comparing it to a completely separate final prediction …

{Prediction Method 2: Offensive and Defensive Rank Differences vs. Points Scored}

I recently discovered a section of Football Outsiders that separately ranks both offenses and defenses in the FBS. For this method, I plotted the rank difference between Team A’s offense and Team B’s defense versus the number of points that Team A scored in the game. This also works in reverse to create a whole separate set of data points; the difference between Team B’s offense and Team A’s defense and the points scored by Team B.^[9] The following chart shows this data for the same set of games, removing games against FCS teams.

This method gives a graph and equation of:

The TCU offense is ranked 10th, while the Hoos defense is ranked 55th. Based on these rankings, the above equation predicts 34 points for TCU, which is exactly the same as the previous method. On the other side of the ball, the Hoos offense is ranked 59th, while the TCU defense is ranked 30th; predicting that the Hoos will score 17 points against TCU, one even touchdown more than the previous method. Method 2 gives TCU 34 – 17 Hoos.

{Conclusion with Intangibles}

TCU appears deadlocked at 34 points, but the Hoos could score anywhere between 10 and 17 on Saturday. I honestly do believe that the Hoos, relieved to see a relatively conventional offense, will play significantly better in this game, scoring on the high end of their projected point total. They’ll be motivated to right the wrongs of last weekend against strong opposition. TCU will also be missing their leading rusher, adding further justification for shrinking the final score margin. The Horned Frogs will pull away after a brief scare from the visiting Hoos:

Final Prediction: TCU 34 – 17 Hoos

Tune in on Sunday for an update to the Spreadsheet of Accountability with a possible Win Probability graph to follow.^[10]

1. ^[1] although not as much as I was during the game, hence the upcoming perspective … ↩
2. ^[2] this was, umm, also written soon after the game. Decided some Jekyll and Hyde tone throughout this post might not be such a bad thing. It reflects the uncertain state of the Hoos season. Or something. ↩
3. ^[3] ending up closer to 600 after GT sent the scrubs in ↩
4. ^[4] at least once we’ve cooled off after watching game (I.e. disregard the level-headedness of my in-game tweets from last weekend) ↩
5. ^[5] attempt to somewhat accurately ↩
6. ^[6] avoiding double-counting the games in which the listed teams played each others ↩
7. ^[7] per the orange line ↩
8. ^[8] the blue line. ouch. ↩
9. ^[9] so even though the third column says “D rank difference,” it’s still from the perspective of the opposing team’s offense. ↩
10. ^[10] assuming this game is actually in question for a decent amount of time ↩

{Initial Score Margin}

I’m going to just use the Massey rankings as my standard this week; I recently discovered that it’s actually a conglomeration of 34 ranking systems running the gamut from from stat-heavy to tea-leaf based.^[4] Massey ranks the Hoos at 54 and Georgia Tech at 43. If you missed last week’s post, the rough idea is to plot the difference in Massey rankings vs. both overall points scored and the difference in final point margin for games in the 2012 season. These plots will give equations that can be used to predict future game outcomes based on a given rank difference. Adding week 2 ACC results to last week’s rank difference chart^[5] gives:

The Hoos are 11 spots lower in average rank, which is the critical input for the two equations derived from the graph below.

The equations predict a 6.46 point loss for the Hoos, who should score 22 points against their old nemesis Al Groh in the process.^[6] I effectively stopped here last week after superfluously discussing some counterbalancing intangibles. This week, though, I’ll add the wrinkle of the Hoos recent success against the triple option.

{Adjusting for Triple Option Success}

Paul Johnson installed the flexbone^[7] triple option at Georgia Tech starting in 2008 after successfully establishing it at Navy. The Hoos have faced the GT triple option four times since then, twice under the oppressively stagnant Groh regime, and twice under London and Reid. Groh, for all of his shortcomings, was extremely successful against Johnson’s offense. Reid et al. have also punched over their fighting weight in games against GT. The chart below shows the final rank difference between the Hoos and Georgia Tech in the relevant year, followed by: the predicted point difference according to our current point margin equation, the actual point difference in the game, and the difference or deviation between the two. Since this game will involve Reid’s defensive coordinating skills, I weighted the games involving Reid double as compared to Groh. The Hoos brass have also discussed the importance of time in preparing for this difficult offense and it’s role in last year’s upset. Consequently, I further doubled the weight for years under Reid without a bye preceding the GT game.

Groh scored a whopping 23 point swing of an upset in 2008.^[8]. Reid also fared well in his second try, guiding his defense to perform one whole touchdown over the predicted outcome. This modification method suggests that the Hoos get a 4.5 point margin bonus against the triple option. After these considerations, Georgia Tech stands at roughly a 2 point win.

{Intangibles}

Last week I considered the Nittany Lions’ wounded pride as an intangible factor that would neatly offset the Hoos home field advantage. This week, the intangible considerations are much more paltry. Groh might have some lingering knowledge that would work in GT’s favor. Conversely, it’s difficult to imagine the Hoos performing as poorly as they did last week; they will be motivated to improve upon their 4 turnover performance. These boring and largely inconsequential considerations should offset, leaving Georgia Tech’s home field advantage as the only influential intangible. Thankfully, fellow math nerds have already calculated the point bonus of home field advantage in college football. We’ll give Georgia Tech the average 3.8 point bonus.^[9]

{Final Prediction}

Adding these up gives a 5.8 point win for GT….^[10]

GA TECH 28 – 22 HOOS

Sadly enough, I largely agree with Heather Dinich, who infamously predicted a narrow 3 point victory for the Hoos …. over Richmond. Bad sign for my prediction, great sign for the Hoos. See you back here after a prediction-shattering Saturday victory over Al Groh and his (relatively) new friends.

1. ^[1] On a side note, this offensive style seriously appeals to my numerical inclinations. The whole strategy is based on probability; give defenses enough angles to cover on one play, and you can run small variations of that play repeatedly and expect to gain the necessary yardage to methodically move the chains. Brilliant in its nearly complete refusal to follow football standards. ↩
2. ^[2] sounds like a great band ↩
3. ^[3] while unfortunately losing quite badly in the other two ↩
4. ^[4] I.e. the coaches poll ↩
5. ^[5] to hopefully add a little more accuracy ↩
6. ^[6] at this point, I’ll admit that this method of predicting score magnitude is not extremely accurate. But it’s better than nothing, and margin is way more important anyway. ↩
7. ^[7] totally not the name I would have chosen ↩
8. ^[8] which could explain why he is now the defensive coordinator at Georgia Tech. On a side note, I spent way too long agonizing over whether to call this post “The Groh Strikes Back” or “Revenge of the Groh” ↩
9. ^[9] according to the description, this site actually calculated specific point values for each stadium, then proceeded to only listed the schools in a rank. Doesn’t make any sense to me. Anyway, GT appears to be higher than the average, so 3.8 is at least a conservative estimate. ↩
10. ^[10] we’ll round it up to 6 since their undisclosed home field advantage is likely slightly higher than 3.8 ↩

This post will graph and comment on the Win Probability of the Hoos throughout the PSU game.^[2] Win Probability (“WP”), as the name suggests, is an attempt to measure one team’s likelihood of winning the game as a function of score, time remaining, field position, and down and distance. The formula is an algorithm combining historical data of various game situations and outcomes. There are a number of assumptions^[3] inherent in this analysis. Nonetheless, the formula should create an interesting result and give us a baseline for comparing various events within the game. An explanation of the WP of a few major plays follows a graph of the overall WP below.

{Hoos Win Probability Throughout Penn State Game}

Big McGee catch #1: The Hoos faced 3rd and 16 on their half of the field on a drive that needed to end in a touchdown to avoid a crippling loss. Jake McGee’s huge one-handed catch single-handedly^[4] improved the Hoos chances of winning the game by 11 percent, keeping our hopes alive.

TD / Big McGee catch #2: McGee struck again, catching a touchdown pass on 3rd and goal from the 6 with 1:31 remaining. As the pass left Rocco’s hand, the Hoos had a 37% chance of winning the game. The Hoos win probability increased to 71% the moment the pass settled into McGee’s grasp. Interestingly, the ensuing Penn State drive leading up to their final FG attempt entirely negated the 36% increase in win percentage resulting from this TD catch.

Final Kick: Poor Sam Ficken and mother nature conspired to contribute the final 69% of win probability the Hoos needed to escape with an as-predicted^[5] 1 point victory.

{Short Postgame Thoughts}

Offense: Rocco was bad for most of the game, but the receivers^[6] weren’t helping either. He also seems to occasionally make his reads from short to long, instead of vice versa; choosing some ill-advised dump-offs. Inevitably, when the bell tolled, he answered, leading the team on his umpteenth game winning drive. I agree with most that the decision to put Phillip Sims in was mind-boggling. The Hoos offensive success also seems to depend largely on Bill Lazor, as they have great drives when he has time to map out a plan of attack.^[7] The offensive line certainly did not live up to expectations, which was a large factor in the lack of running game success.^[8]

Defense: Very, very good performance against the decidedly weaker unit of Penn State’s team. Coverage was good, blitzing was timely, and they cleaned up myriad offensive mistakes.^[9] Nicholson and Greer had noticeably fantastic games. Drequan Hoskey was a bit over-aggressive on a few tackling opportunities. The heralded Eli Harold also made a small contribution.

Look for a Georgia Tech preview coming later this week.

1. ^[1] Also keep in mind: this Penn State defense should easily be highly ranked at the end of the season. The offense was bad, but with good reason. ↩
2. ^[2] much like the inaugural post of this blog inspired by the wacky FSU game ↩
3. ^[3] Some assumptions: 1. The home team starts the game with a WP of 53%. I borrowed this number from baseball, where advanced statistics are more developed. Probably more accurate than just calling it 50%. 2. The model assumes that only score difference, and not score, matters. Imagine a game where Team B is at the opponent’s 40 with 2 minutes remaining in a tied game. Would you think they are more likely to score if the game is 35-35 as opposed to 0-0? Is this just perception or reality? ↩
4. ^[4] oh, puns ↩
5. ^[5] didn’t think I’d let that go for the whole recap, did you? ↩
6. ^[6] outside of McGee ↩
7. ^[7] like the first drive of the second half ↩
8. ^[8] although again, the Penn State defense, specifically its linebackers, is very good ↩
9. ^[9] although some of their success can be attributed to the ineptitude of Sam Ficken ↩

This post will attempt to predict the outcome of the Hoos’ Saturday matchup with the Nittany Lions. Like anyone with a TV and/or Internet access, I’m well aware of the scandal and subsequent fallout surrounding Penn State. Still, and maybe it’s just my interminable proximity to Penn State’s consistent football success, but I’m puzzled by the overwhelming confidence in a Hoo victory.^[4] Vegas currently predicts that the Hoos will win by 10. Ten point favorites against a team that has spent nearly as much time inside the top 25 as the Hoos have spent outside it.

Turns out I’m not the only one who thinks the Lions aren’t completely toothless;^[5] below is a ranking comparison of stat-heavy college football blogs:

… which neatly brings us to an explanation of the game prediction method. It’s difficult and inaccurate to project performance based on one game. Instead, I’ll use the overall rankings shown in the chart above, determining a relationship between current ranking difference for all ACC and Big 10 teams vs. their week 1 opponents and week 1 results.^[6] I’ll also trace ranking difference versus raw points scored to project point magnitude in Saturday’s matchup. The Hoos average ranking difference as compared to Penn State will then give us a predicted final score.

{Predicting Score from Rankings and ACC / Big 10 Week 1 Games}

The chart below shows the ranking difference vs. opponent, final point difference from the perspective of the listed team, and points scored in the week 1 game. For example, the Hoos are currently ranked 67 spots ahead of their week 1 opponent Richmond,^[7] won by 24 points, and scored 43 points in the process.

Ranking difference versus points difference (blue) and points scored (orange) are plotted in the graph below.

The equation derived from the point difference plot actually predicts a 1 point win for the Hoos, who sport an average ranking 11 spots lower than Penn State. The projected win despite the lower ranking could be due to some early systematic overrating of recently successful programs that are not quite as good as they seem.^[8] The points scored equation predicts that the Hoos will score 25 points against Penn State.^[9] This model thus predicts a 25 to 24 Hoos win.

{Accounting for Error / Intangibles}

But not so fast; as we saw in week 1 action, these rankings and their relationship to game outcomes aren’t precisely accurate. So I sought to derive an error range to apply to the predicted score. I determined a deviation between what the aforementioned model would have predicted, based on the Massey and Sagarin rankings, and the actual score for each of the Hoos’ and Nittany Lions’ week 1 games.

The chart shows an overall average error of around 10 points in the final score. Maintaining our 25 point Hoo production, this suggests the final score could fall anywhere between a 25-19 Hoos win and a 29-25 Hoos loss. Some good old fashioned analysis of intangibles should help us decide where the final score will fall within this range.

The home setting obviously favors the Hoos, who are eager to prove that last season was not a fluke. This game against a proverbially wounded major program is a golden opportunity to landmark Mike London’s progress. Further, many cite Penn State’s upheaval as a reason for a substantial victory. My persistent sports pessimism allows me to see this the opposite way. Penn State is a proud program that will be extremely motivated to provide an uplifting victory for their troubled fanbase. Emotion might help, not hurt, these cornered Lions as they seek to avoid circling the drain before the season even begins.^[10] I’m tempted to pick a Penn State victory.^[11] Their flat week 1 loss to Ohio and the prevailing popular opinion suggest that would be foolish. With great hesitation, I’ll take the middle road and assume that home field advantage and the unstoppable Mike London factor cancel out the Nittany Lions’ desperation.

{Final Score Prediction}

HOOS 25 – 24 PENN STATE

In all honesty, I hope those merrily predicting massive Hoo victories are absolutely correct, and on Saturday evening can scoff at my nervousness heading into this game. Until then, I’ll be over here wringing my hands.

1. ^[1] along with our hero Matt Schaub (as I choose to forget that he went to the other high school in my town) ↩
2. ^[2] thankfully I somehow avoided becoming a fan ↩
3. ^[3] within reason, of course ↩
4. ^[4] at CBS and from our good friend Heather Dinich ↩
5. ^[5] … sorry. Also, as a side note, I have full confidence that cavman will prevail in his pregame matchup with the animated Lion himself, but not before finding himself in a veritable pickle ↩
6. ^[6] if this doesn’t make sense yet, you’ll see below. ↩
7. ^[7] the Sagarin and Massey ratings included both the FBS and the FCS; Sagarin steadfastly referring to them as 1A and 1AA ↩
8. ^[8] Auburn and Boise State in week 1 ↩
9. ^[9] seems a little high, but I’ll stick with it ↩
10. ^[10] personal foul: excessive metaphors ↩
11. ^[11] if the Hoos actually do win big this weekend, I’m just going to assume that I have been the victim of profound Penn State brainwashing ↩

It’s what you’ve all been waiting for: the last post in a seemingly never-ending series predicting myriad aspects of the Hoos 2012 season! Oh … and the first game is on Saturday too. This post will gather all the calculated percentage changes for various Hoo positions, do a little magical, numerical hand-waving, and end with a predicted record for the 2012 season.^[1]

As the wide receivers must rely entirely on the passing abilities of the quarterbacks,^[2] it makes sense to average these two performance percentage improvements before an overall calculation. This yields a passing improvement of 5 percent. The running backs solely control their portion of the offense, adding a rushing improvement of 7 percent. The offense overall should thus improve by 12 percent. The defense, as stated in the points against projection article, should face a drop in performance of about 11 percent due to the significant loss of experience. Some rather complicated math then yields an overall change of +1% as compared to the 2011 version of Hoo football.

When initially planning this post, I intended to adjust the final percentage change based on the strength of schedule difference between the two seasons. However, the end of season 2011 strength of schedule ranking was 58; the preseason strength of schedule rank for 2012 is 70.^[3] Given that’s it’s mostly guessing anyway this fine difference isn’t enough to make a distinction affecting predicted 2012 performance. We’ll stick with the +1%, which is not nearly enough to justify adding an extra win to the final record.^[4]

The Hoos will finish the 2012 season with a record of 8-4. Just like last year. The offensive improvement is substantial enough to offset the defensive deficiencies. Most pontificating pundits have predicted the Hoos to slip to at least 7-5. We’ll see how the numbers-intensive approach holds up.

As the season progresses, look for individual game predictions and other fun stat-based analyses; perhaps some analysis of in-game decisions and changes in win probability.^[5] Also look for a grand “spreadsheet of accountability” that I’ll put on a permanent page and update with my predictions, Vegas predictions, and results.^[6]

And finally, a huge WAHOOWA to kick off a new season for a program on the rise!

1. ^[1] magic. Kinda like this. ↩
2. ^[2] well, for the most part ↩
3. ^[3] ranks here ↩
4. ^[4] with a 12 game season, the Hoos would need about a 7% improvement to finish with one more win ↩
5. ^[5] win probability from florida state last year, for example ↩
6. ^[6] the nerds vs. the goodfellas ↩

Optimism abounds at the start of any new season.^[2] Indeed, most of the preseason Hoo fan hand-wringing has come in response to a choice between two very solid QBs who make 2006 starter Christian Olsen look like … well, Christian Olsen.^[3] OC Bill Lazor even went on to state, with perhaps some unintentional bravado, that his biggest concern entering the season is “depth along the offensive line.”^[4] But as usual, the Hoos will also need to go out and play some defense in order to win games.

Most fans understand that the defense is replacing a number of starters and probably shouldn’t be relied upon to post consistent shutouts.^[5] However, many starry-eyed Hoo fans have avoided acknowledging the truly dramatic lack of experience facing the defensive Hoos in 2012.^[6] The Hoos have five returning defensive starters. Five; as in, out of eleven. How many times, you might ask, over the past ten years have the Hoos had five or fewer returning defensive starters? ZERO.^[7] The Hoos have had six returning defensive starters four separate times over the past ten years, but never five. In fact, the last time the Hoos had this much defensive upheaval was way back when Weezer was making relevant music.

This post will use the aforementioned nerve-wracking figure of returning defensive starters to project the overall defensive performance as compared to 2011. I’ll use two separate methods of arriving at overall points surrendered, averaging them to arrive at a predicted points figure for 2012.

{Method 1: Number of Hoos Returning Starters}

For the first method, I plotted the number of returning starters over each of the last ten years versus the total points surrendered. Then^[8] I traced the plotted points to obtain an equation relating number of returning starters for past Hoo teams to total points surrendered.

The key figure for comparison for both of these methods is 267; the total points surrendered in 2011. Plugging five returning starters into the above equation yields 304 total points against in 2012. Since the Hoos have not had five returning starters in the collected data, though, for the sake of accuracy I also devised a separate method …

{Method 2: Constant of Five Defensive Returners}

For the second method, I found twelve FBS teams from 2011 with five returning defensive starters, setting this low number of returners as a constant against the total points surrendered.^[9] Instead of just averaging the total points against,^[10] I plotted a measurement of team quality versus the points. Both overall team rank and strength of schedule should affect the points surrendered. To account for both, I subtracted a team’s strength of schedule from its overall rank.^[11] The more negative the resulting number, the more likely the team in question surrenders fewer points.^[12] I plotted this number versus points surrendered in 2011.

The Hoos have a 2012 preseason rank of 35 with a strength of schedule of 51. The resulting figure outputs 287 points surrendered in 2012.

{2012 Defensive Performance}

The two different methods resulted in startlingly similar predictions for total points. The separation of 17 points means the two models differed by about 1.4 points per game.^[13] As for a concrete prediction, I’ll go with the average of the two, 296 points against in 2012; an 11 percent drop in overall defensive performance as compared to 2011.

Throughout this series we’ve seen some decidedly optimistic offensive growth predictions, which are now tempered by an unnerving defensive drop. In the final post^[14] I’ll combine the individual predictions into one final record for the 2012 Hoos.

1. ^[1] hopefully appearing before September 1st … ↩
2. ^[2] especially if you read all of my offensive position season preview posts ↩
3. ^[3] much fun is had at Mr. Olsen’s expense. The Olsen era was (very) brief, but emotionally scarring ↩
4. ^[4] undoubtedly important, but how many teams would love to say that this is their biggest problem? ↩
5. ^[5] Then they go back to debating the merits of the various quarterbacks. ↩
6. ^[6] kudos if you’ve been focusing your hand-wringing in the right place ↩
7. ^[7] see what I did there? ↩
8. ^[8] as those who regularly read this blog were undoubtedly expecting ↩
9. ^[9] thought this was a pretty cool idea ↩
10. ^[10] would be too straightforward ↩
11. ^[11] both from preseason, since that’s what we’re comparing to for 2012 ↩
12. ^[12] negative is good, like golf ↩
13. ^[13] 25.3ish to 23.9ish ↩
14. ^[14] appearing … sometime before Saturday ↩