# Recap and Win Probabilities vs. Penn State

Yesterday’s game was an absolutely crucial win in setting a successful course for the 2012 season. With upcoming away games against Georgia Tech and TCU, a loss against Penn State could have set a negative tone easily leading to a hole-digging 1-3 start. It is critically important for every successful team to scrap through at least one flat outing. These sub-par performances are going to happen in every season, so when they come against a solid opponent, emerging victorious is what separates quality teams from those on the cusp. I can’t emphasize this enough; if the Hoos go on to match the success of last season, the ugly Penn State win will be one of the prettiest in hindsight.[1]

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

# Season Preview – Offense: Receivers, the Critical Unknown

[This post is part of a season preview series that breaks down the offense and defense in multiple sections. Each section will end with an attempt to both evaluate which players should start and quantify a predicted change in performance from last season. The last post will sum up the series,[1] giving a final prediction of the team record for 2012. The following is the fifth section of offense.]

While the QB controversy rages on, causing indiscriminate spilling of 0s and 1s across innumerable Internet sources, one critical aspect of passing proficiency has gone overlooked; passes rarely result in huge gains without players there to catch them. The 2012 receiving Hoos are largely a heterogeneous mixture of wily veterans and heralded recruits ascending into bigger roles. The success of the Hoos offense will depend significantly on the growth of this unit. As with previous posts on the quarterbacks and running backs, this post will attempt to predict the statistical growth and performance of the Hoos pass catchers in 2012.[2]

I decided to formulate my own receiving statistic as an overall measure of production, with the hope that boiling down separate statistics into one number would streamline the process of tracing player improvement.[3] Receiving Skill (RS)[4] is an attempt to combine (1) ability to get open and make a catch, as measured by catches per target, with (2) the ability to get down the field, as measured by total receiving yards. Each of these abilities is critical to overall receiving production. Consequently, in order to give them equal weight, I placed catches per target on a similar scale to yards by multiplying the ratio by 1000. RS is then simply these two values added together.[5] Simple, but hopefully effective.

In order to prevent this post from droning on excessively[6], I limited the pool of possible receivers, by last year’s total yards, to the top three wide receivers, the top receiving running back, and the top tight end. These top five accounted for nearly 80% of the receiving yards in 2011. The 2011 RS chart appears below:

The general plan: formulate a similar RS chart for 2012, total the respective RSs, and calculate the percentage difference between the two seasons. But first, we need equations governing the expected progress for the various receiving positions.

{Growth Equations}

I only used yards to determine growth, effectively considering catches per target as a constant throughout the college career. My initial attempts at tracing the data suggested that there was no discernible pattern, and really, I wouldn’t expect raw pass catching ability to improve.  Drops and spectacular catches happen at every level of football. Assuming an NFL and a college receiver stood at the same distance from a pass catching machine, would the NFL player necessarily catch more passes?[7] A slight advantage for the NFL player could be reasonable, but this small skill progress would likely be consumed by the other factors that affect catches/target, like poor QB decisions. Drops are also too few to create an appropriate sample size of catching ability.[8]

Total yards, by contrast, are a factor of attention paid and a player’s growing importance within the game plan. Concentrating on one statistic also allowed me to emphasize the number of contributing growth patterns[9] as opposed to the complexity and numeracy of different statistics. Theoretically this should improve the accuracy of the predicted growth.

Measuring yards per season growth for representative Hoos over the past few years yielded the following graph:

As in the QB and RB iterations, I plotted the year of experience vs. the percentage differences from the above chart, giving the graph and equations below:

{2012 Improvement}

The equations from the above graph contributed to the final chart below. The calculations were completed for Jones, the top RB receiver, Smith, Jennings, and Terrell, the top three returning WRs, and Freedman, the top returning TE. The 2011% and 2012% columns are determined by the player’s position on the growth curve in their corresponding year of experience.[10] Note that the percentage change column is half the 2012 yards growth percentage due to the catches/target constant.

We can expect an overall improvement of 5 percent among the Hoos pass receivers as compared to 2011. Although the Hoos lose Burd’s top production from 2011, the growth curve suggests that the inclusion of Terrell in the top 5, along with one more year of experience for the other four pass catchers, results in a net gain for the passing attack.

Interestingly, this model anticipates that Jones, a running back, will produce the highest overall receiving production. In past years this would certainly be believable. However, with the coaching staff voicing an intent to expand the downfield passing game, we’ll see if this is accurate. The model also suggests that Terrell and Jennings, two highly regarded recruits, are entering a breakthrough season to become a significant part of the offense after only minimally contributing in their first year. It will be fascinating to see if these two players can take a step toward fulfilling their enormous potential.

All major sections of the Hoos offense should improve in 2012.[11] Although the offensive progress signals a potentially big season for the Hoos, the defense is young and inexperienced. Check back early next week[12] for an analysis of defensive growth.

1. [1] hopefully appearing before September 1st …
2. [2] this time in one efficient post … since the previous ones cover much of the necessary explanation
3. [3] or at least I think i made it up … decent chance that someone has done this before
4. [4] original names are not exactly my strong suit
5. [5] RS = [(catches/targets)*1000] + yards
6. [6] ahem, we won’t mention any other offending posts by name
7. [7] again with the rhetorical questions
8. [8] at the end of this paragraph I realized I had promised to not go on any statistical tirades, oh well
9. [9] I.e. the number of players creating the graph
10. [10] for example, Jones (the orange line) was a third year last year, putting him at 0%, and will be a fourth year with 59%
11. [11] sorry linemen. You’re important, but difficult to measure statistically. The hope is that your contributions are accounted for buried somewhere among these rushing and passing stats.
12. [12] just in time for the first game

# Season Preview – Offense: The Three-headed RB Monster, Part II

[This post is part of a season preview series that breaks down the offense and defense in multiple sections. Each section will end with an attempt to both evaluate which players should start and quantify a predicted change in performance from last season. The last post will sum up the series,[1] giving a final prediction of the team record for 2012. The following is the fourth section of offense.]

Part I left off with a graph and equations that trace the predicted performance of Parks and Richardson in 2012. The next step in fully rendering our three-headed RB monster is predicting Superman Jones’ improvement. After quantifying the fourth year’s growth, the post will conclude with an analysis of the RBs individually and as a unit.

{GROWTH – PERRY JONES}
I used the arc of Wali Lundy’s distinguished career as a Hoo to predict Jones’ 2012 statistical fortitude. As with Parks and Richardson, I plotted and traced both yards per carry and yards per game, this time over three years of solid production.[2] Jones will likely play games 26-39 in the graph below:[3]

Keep in mind that these graphs are a percentage over the mean. To wit, I was initially shocked that the projected Hoo RB career takes a dramatic plunge in yards per game in the last year. But in this graph the decline really only happens after game 31, when the curve dips below 0%. Prior to this point, despite the ominous negative slope, the percentage is still positive, indicating continued improvement.[4] We’ll use the growth graph for both sets of RBs in the dramatic …

{RB CONCLUSION}

[Yards per Game]
We’ll tackle yards per game first because this stat is nowhere near as standardized a predictor as yards per carry. Yards per game fluctuate wildly, even in a very successful season, and depend more on number of carries than skill.[5] The precision of the predicted yards output here, as compared to predicted yards per carry, thus analogizes well to the U.S. Men’s to Women’s Gymnastics performances in the team competition; the yards output has some promise, but will likely end up proverbially sitting on the pommel horse.[6] The analysis should nonetheless be interesting and might help us at least figure out the distribution of total yards.

The previously formulated graphs and equations helped create the chart below. I traced predicted yards per game in the first game, the mid-season game, and the last game. The bold column is a prediction of yards per game average for the whole year.

There is an error[7] in Parks’ column; his overall yards per game should be 63.89, not 83.89. [8] This decreases the yards per game for the corps as a whole to just slightly more than last year (166.30). The percentage distribution of yards in 2012 then becomes 38%, 24%, and 38% from top to bottom.

As you’ve probably noticed, we’re splitting hairs here by saying that Parks will improve by 4 yards per game and Jones will produce 8 fewer yards per game. But it might not be so unbelievable. London and Lazor have stated a desire to expand the passing game in 2012. A more robust passing attack coupled with an improving, already talented, running back group could effectively offset each other. By emphasizing the passing game more, perhaps Jones sees even more short passes, freeing up carries for rising backs who definitely deserve a greater role. We’ll see. The yards per game figures might not be too far off, but I’m not holding my breath.

[Yards per Carry]
The yards per carry graph below was created with the same equation procedure.[9] We’ll use this stat as our defining improvement metric since it is the more standardized measure of skill.

A one might expect, the two younger backs have a sharp increase in yards per carry to start the season, but tire as the games wear on and the opposition improves. Parks and Richardson will both improve about 2% overall. Superman, on the other hand, is all rise throughout his last season; improving about 17% overall. The Man of Steel will prove that decline is only for those not from Krypton.[10]

These measurements suggest an average overall improvement of 7% for the running backs. Consider that there are about 13 games in a bowl season. Each game is then 7.7% of the season. The running back growth alone is effectively worth a whole game of improvement on the Hoos’ record. Following the 2% improvement for quarterbacks, the offense is looking quite strong.

Check back for a special report from next week’s live practices, where I will actually need to go outside and observe football in order to create blog content. An analysis of the inexperienced, über-talented pass catchers will follow.[11]

1. [1] hopefully appearing before September 1st …
2. [2] I was tempted to go on my non-use of touchdowns soliloquy again here … I’ll spare you this time
3. [3] again assuming the Hoos can at least find their way into the Beef O’Brady’s Bowl. On a side note, I find myself quite confident in this reaching of bowls assumption, despite last year being our first trip since Jefferson himself graced Monticello. Just call me a hopeless optimist.
4. [4] slowing improvement, but improvement nonetheless …. Ok, i’m pretty sure you get this now, I’ll stop explaining
5. [5] although there is an obvious correlation there
6. [6] zing!
7. [7] gasp!
8. [8] Kind of a long story, but basically I took a screen shot of the table, then promptly lost the data in the excel file that created it. An regrettable and unfortunate transcribing error
9. [9] except no errors this time!
10. [10] Krypton was him home planet; I looked it up. So it’s kind of weird that he was deathly allergic to a mineral that presumably comprised his home planet. Although I’m allergic to grass and trees …. so I guess that makes me Superman.
11. [11] I.e. the wide receivers, tight ends, and running backs; since Lazor likes to spreading around.