no wonder we hired Hunter Lochmann
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(Link in case I suck and the video doesn't show)
Obviously we would change Ronaldo for a current M player (Forcier maybe?) and change the "plays on the left" part to something different (he throws/runs/rolls to the left/right?) and finally change the England to whoever we're playing that day. Maybe sing it after a TD. I figure it's something simple that most people would be able to pick up on after hearing it once or twice.
He throws to the left
He throws to the riiiiight
That boy Tate Forcier makes State/Iowa/Ohio State look shite
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Since there are a lot of lines in this one, maybe just do the Na Nar parts and maybe put in "Miller, Ricardo Miller, Michigan's #1" after good grab or "Turner, JT Turner, Michigan's #2" after an INT. Again, it's pretty simple and most would be able to pick up on it.
I'm not limiting us to those but they're pretty easy to learn and adapt. I really like it as long as it's something simple and that it could very easily catch on since it would unique to college football (I think? I can't think of a school that does it).
What do you guys think, good or bad idea? Anyone else have a good chant to use, maybe something more original? Please no actual soccer talk; I don't want to start a flame war.
**Note** - I love The Victors, Temptation and all that good shit and I am by no means trying to say it isn't good enough. I was shooting for more along the lines of player specific like the examples I posted.
***Edit - This is my first diary post and I don't know why the videos won't show. I've posted videos in the forum. Anyone want to help a brother out?
First off, I largely agree with ikestoys's diary (http://mgoblog.com/diaries/down-14-and-going-2). I have often thought that football is a game that rewards aggressive play calling, like going for two and on fourth down more often, and fake punts from your own 20... Eh...
Anyway, I disagree with a couple of points ikestoys made, both explicitly and implicitly, and I thought I'd chuck 'em out here.
Trials are not independent
This point was made by a commenter in the original diary, but the basic idea of treating the different sorts of trials (going for 2, going for 1, overtime) as independent events (and therefore as amenable to the application of the mathematics of garden-variety probability theory) is flawed.
In football the outcome of one trial affects the probability of another trial even occurring, and not in predictable ways. Let's say UM had made the first two-point conversion. Would State have played their next drive differently than they did? Maybe, maybe not. Perhaps they would have come out throwing and scored a field goal to go up by nine. We have no way of knowing how things would have unfolded in that alternative universe.
Relative frequencies are not probabilities
Second, and another point made by a commenter, is that ikestoys treats relative frequencies (the proportion of successful two-point conversions) as the same thing as probabilities of success. They are not. That's like saying that because 1% of adults die of lung cancer, you have a 1 in 100 chance of dying of lung cancer. Do you smoke? If so, then your probability is surely higher. If not, it's lower. The point here is that the probability of success of a two point conversion depends on many factors, as various people have noted.
Because relative frequencies =/= probabilities, I thought it would be interesting to see how the probabilities of winning fared if you didn't assume the probability of a successful two-point conversion was 0.44. So, two graphs for your viewing pleasure. The y-axis is the probability of winning the game after all events have unfolded (post-touchdown try after TD 1, TD 2, and possibly overtime). The x-axis is the probability of success of the two-point conversion (I limited the range of this probability to between 20 and 80%).
Graph the first
In the first graph, I have plotted the cumulative probabilities of winning for two strategies: going for 2 after scoring a TD to be down by 8 (iketoys's strategy--the black line), and going for 1 (RichRod's strategy--the blue line). The only thing I have allowed to vary is the probability of success of a two-point conversion (on the x-axis).
- Note that I have reproduced the probability ikestoys does, where the dashed red line intersects with the black curve at about 57% when Pr(success) for a two-point conversion is 44%.
- Note also that despite ikestoys's implicit claim that going for two is always the better move, if the probability of success falls below 35.5%, it is better to go for 1, as RichRod did. I'm not suggesting that this is what the probability would have been, though people's comments about a dog-tired Tate, a driving rain, etc., make this idea not too farfetched).
There are two other variables in the process: the probability of a successful PAT (which I held constant at 0.95), and the probability of winning in OT. The latter probability doesn't change the black curve below much, so I left it at 50/50, as did ikestoys.
In the graph below, the three non-black curves represent three different probabilities of winning in overtime: 40% (orange), 50% (blue), and 60% (green).
The only thing to take away here is that if you believe your probability of winning in overtime is high (based on your style of play, being at home, etc.) and if you believe your probability of a successful 2-point conversion is less than 44%(ish), then you should adopt RichRod's strategy. If you believe that your chances of winning in OT is 50/50, and you believe your chances of scoring on a two-pointer are > 35%, then you should follow ikestoys's strategy.
In conclusion (I know, finally)
Of course, coaches don't think this way in the heat of a game. Again, I basically agree with ikestoys, but the story is a bit more complex.
For every down, distance and yardline I have a calculate expected value. The expected value equates to the average points scored from an average team in that situation.
*Example, 1st and 10 at your own 20, no situation has more data points than this one. Last year, this situation yielded an average of 1.57 points every time it occurred. Obviously, you can't score 1.57 points in a football game. If you had the ball in this situation 100 times, you would score 157 points. It could be a TD every 4-5 possessions or a FG every other possession or probably some mix.
Each play changes the that expected value and that value is then attributed to the player/players who were recorded on the play. Over the course of games and seasons these points add up, some positive, some negative and we begin to see a clearer picture of what value was added by what players/units.
But adding value isn't the same for all opponents. A total of +10 is a very impressive number, but its more impressive against a good team than a bad team. After all of the data is collected, every team's unit is rated on a per play basis. This value is then added or subtracted from every play that occurs against it.
*Example, a good rush defense averages -0.1 against it every time the opponent runs. They are playing a decent run offense that averages +.04 every play. If the net result for the game is a -5 on 40 carries, the adjusted results would be a -1 rating for the offense (-5 + 0.1*40 = -1) and a +6.6 rating for the defense (-[-5 - 0.04*40]) in my write-ups, positive is always above average and negative is below average.
So the essence of the metric is how many scoreboard points did the player/unit contribute vs average and accounting for competition.
Exceptions and Notes
- Plays with lost fumbles are removed from all numbers because fumbles are considered random and greatly skew ratings
- QB sacks are included for team passing metrics but not for individual players
- Garbage time is not included in stats. If a team is up by 4 TDs in the 3rd quarter or 3 in the 4th it is considered garbage time and no plays are recorded.
- Wide receivers have 2 ratings, a rating on balls caught (Value) and a rating on balls caught or on balls targeted at them (Value+) the two metrics tell two different things and I haven't figured out how to combine them. WR values typically run higher because of the lack of negative plays assigned directly to a WR.
- Performing on third down is huge, on third down you either make a first down and you gain big points, or your drive is over and you lose any points expected for the drive (unless in FG range). This is one of the big advantages of this system, it can reward/punish plays made on big downs appropriately
- Only games against 1A competition count. Games against 1AA teams are basically scrimmages with nothing good or bad counting.
- All data is pulled directly from play by play data hosted on the NCAA website. I load all the data into a SS, run a bunch of fancy formulas and then dump it into a database where I can run queries till I pass out or the boss shows up.
It is scary to put this in writing, but here are my goals.
Monday - Game Review
Tuesday - Big 10 Player Rankings
Wednesday - Big 10 Team Rankings
Thursday - Flex/Catch up if I missed a deadline
Friday - Game Preview
During the offseason I am looking for ideas to pull from my DB of plays to validate or refute conventional wisdom. Items such as, is momentum real on quick change plays? Examining 4th down convention. Etc, again, looking for ideas.
Ideas going forward
I am very open to ideas anyone has on how to improve what I pull, how its calculated or what I do with it. Also, I am working on moving from expected points to a win percentage calculator so that there is no need for garbage time gray area. Won't happen this year but hopefully next year I will have that added.
Any questions on how By The Numbers works, look here.
This was called out as a 3 point win for Michigan going in. It wasn't. State absolutely shut down the Michigan running game, the final number -4 for the game, but it was worse than that.
Minor was a -2.2 on 4 carries, none of them with positive values. Carlos Brown was -2.0 on 6 carries, with only 2 carries coming out positive. Shoelace posted a pair of negative runs. Odoms one carry was a negative. The only one who did anything on the ground was Forcier (+1.7) and most of his value was on scrambles. 6 of his 11 runs (sacks are excluded) went positive which is not great but for the rest of the teams performance, was far above average this week.
In the preview this was noted as the biggest disparity of any unit in the game. Excluding Stonum's fumble, the passing game was slightly negative (-0.6). The three sacks cost nearly 4 points on the game and the interception in OT was obviously a big deal.
Stonum, apart from the fumble, had a very nice game with 1 point on 3 catches plus over 5 points on the long TD which more than offset the lost value on his fumble.
Matthews was targeted 5 times, none of them complete. We'll have to wait for the UFR to see where the fault lies.
Hemingway picked up 3 catches for a meager +1.
Kogar was 1/4 on targeted balls, but snuck into the positives at +.6.
Odoms had a productive day, picking up nearly 3.5 points in 6 targets with 4 points coming on his 5 catches.
Roundtree's grab in the endzone was worth 3 points after missing on two targets that worth 1.5 points.
Saw this as a point or two disadvantage coming in, and that's about where it netted out, although in a strange way.
Jailbird Glen Winston was -8.6, even with his +3 TD run.
Larry Caper's game deciding run, just put him back to even on the game.
Running QB Keith Nichol ran only twice for little value.
Wow! Kirk Cousins, 5 rushes, all for positive value racking up an incredible +8 on the ground for the game. Wow!
What a bizarre path to an expected outcome.
This was the only segment that was a solid win for Michigan. Coming in, it was expected to be a 2 point disadvantage but it ended up being a 2 point advantage.
The two sacks netted a three point advantage for Michigan, even when taking out the benefit of the fumble.
Strangely enough, Keith Nichol (+2.4) added more value through the air than Kirk Cousins (-.4) did on the day.
Cunningham was the only receiver who managed to post more than a point or two of value for the game with a +7.3.
Even with the turnovers, Michigan failed to have an advantage in field position. The regulation numbers for field position where 24 exp points for MSU and 22 for Michigan. The first down at the 25 for OT is worth just over 4 points, so for the game, MSU scored 1 less point than expected (PAT is assumed) after being -4 in regulation while Michigan was -2 in regulation but -6 on the game.
Other the Zoltan audible, the special teams didn't provide any huge advantages for either side.
Olesnavage had another good game, going +1.9 on his two field goals while Swenson was +.6 on two chippies.
MSU had a +.5 advantage on kickoff teams with Michigan giving up a couple of good returns.
When Michigan was actually punting, the punt teams netted out with no real advantages and MSU had negligible advantage in returns.
A lot of value came Michigan's way via the yellow laundry on Saturday. NO CONSPIRACY!
When Michigan was on offense there was a net pickup of a point of value due to penalties. However, MSU's offense had the should have been killer penalty problems, costing them 5.5 points in value most of which coming on...
The Drive that spanned the globe
Thought I would add a little note on the drive that covered a ton of yardage and could not be stopped.
During the drive, Michigan State rushed 10 times for a value of 3.8, 3 of which coming from Cousins. The Spartans passed 7 times for a value of 5.9. That's 17 plays, adding nearly 10 points in value. Obviously, this was all to offset the ridiculous penalties being accumulated during the drive. Michigan State's 4 penalties cost them 4.3 points on the drive. After the second personal foul, Michigan State's 2nd and 25 put their drive expected points at 1.4, less that what they would have expected when they started the drive. A very strange drive.
For those that do not know, Mealer is a RS freshman from Wauseon, Ohio that sat out last year due to a torn rotartor cuff. His family and girlfriend was in a car accident on Christmas Eve 2007. The car was struck on the driver side and both his father and girlfriend were killed during or soon after the accident. Additionally, Elliott's brother was on the passenger side front and was trapped inside the car. Elliott tried to rescue his brother from the car and this is how he tore his rotartor cuff. Soon after the accident Elliott's brother was claimed to be paralyzed from the waist down.
The accident happened one week before Carr retired. When RR came in he was very accepting of Elliott's condition and circumstance. RR told Elliott that no matter if he can ever play a down for Michigan, he will always have a scholarship at Michigan. Elliott's brother is also making progress, I heard today that he is walking with assistance now and is swimming.
This is a great story, reminds me of the ESPN make a wish stories or something like that. I hope everyone gets a chance to watch this. I don't know if it is because I am from Toledo, Ohio (not that far from Wauseon) but I am really pulling for Elliott and his family.
Here is a link to the story by a Toledo news station.
Also a link to the mgoblue.com page (check out the two videos...great stuff)
Given all the talk before the Michigan – Michigan State game about “respect” as well as under and overrated teams, I found myself wondering which team in the Big Ten is typically the most overrated. I feel like MSU never meets their preseason expectations, but the same can usually be said about Michigan as well. Ohio State has been very solid within the conference, but obviously slips up once it goes up against the elite outside the Big 10. But which team is the most consistently overrated during the entire season?
I analyzed the point spreads for all Big 10 games for the past 10 years, 1999-2008. Team performance against the spread is well documented, but we don’t really care about ATS; hanging 50 points on Wofford when the spread is 40 does not an underrated team make. We want to break the lines down to victory or defeat, and see how the team performs in comparison.
W # of upsets against opposing team
L # of times being upset
TOT Total deviation from expected over total games
Stdev Standard deviation
PW Predicted win percentage
ATS Performance against the spread
46% Penn State
51% Ohio State
53% Michigan State
Move over Mark Dantonio, there is a new Rodney Dangerfield in town. Yes, humble Northwestern earns the title of most misunderestimated in the Big 10. Meanwhile, Purdue is the most overrated team, although they have several teams not far behind, notably Michigan. I expected the Big 10 bottom-feeders to be the most underrated; when everyone expects you to lose every game, there is nowhere to go but up. Likewise, the big boys would be near the top. But Purdue has no excuse ... they have been given the modest task of winning 2/3 of their games, and they consistently blow it.
- Ohio State has the highest predicted win percentage @ 83%, as well as the lowest standard deviation. People expect them to win, and they oblige.
- Michigan State appears to perform as-expected @ -2.5%, but they have the highest standard deviation. Sparty wins a lot of games they have no right to win, and loses a lot of games for no reason, and basically acts very Sparty-like
- The third most underrated teams is Iowa. Given Michigan's overrated-ness, this does not bode well for this Saturday. Or it has no relevance, since Iowa is already favored ... I haven't decided.
- Purdue has only upset 6 teams in the past decade. Northwestern performed the same feat between 2005 and 2006.
- I expected the ratio of underrated teams to overrated teams to be closer. The Big Ten, not surprisingly, is not performing.
Thoughts? Is this a useful analysis of overrated-ness? Should this be expanded to additional seasons and teams? Spoiler-alert: I have already looked at Notre Dame, and they are not, repeat NOT the most overrated team in the universe.