"...don't believe something because an "expert" is saying it. Believe it because of the evidence."
Just a quick refresher from week 7 game vs. Iowa. Diary can be found here: http://mgoblog.com/diaries/post-week-6-yardage-analysis-and-predictions-... . UM's offense and defense both came to play against Iowa. The offense was predicted to put up 407 yards of offense. They put up 522 yards. This was UMs best offensive day of the season, posting a 200% of Iowa's season average. This moves UM season average up to gaining nearly 159% of what their opponents typically give up.
The defense also had a stellar day giving up 383 of the predicted 454 yards of Iowa offense. Statistically for the defense, this was their best game of the season. They held Iowa to 91% of their season output. This was the third time this season the UM defense held their opponent under their season average.
The score predictor nearly hit a grand slam two weekends ago. I predicted a 35-28 UM loss. The final score was 38-28 Iowa. Despite UM having a great day moving the ball and having a relatively decent day holding Iowa in yardage, they did not succeed where it counts: on the scoreboard. Iowa was scoring 1 point every 12.69 yards on the season. In the Michigan game, they scored 1 point every 10.08 yards; well above their season average. Iowa had damn good field position all game and plays like Sash made on the block FG returning it past midfield, really hurt UMs chances in this ballgame. Not to mention the TWO!!! kickoffs out of bounds.
Where was UM on yards per point? For the season, UM was scoring a point every 13.75 yards. During the Iowa game, UM scored one point every 18.64 yards. A product, no doubt, of penalties and turnovers. UM, after the Iowa game, is score a point every 14.78 yards. Something interesting to note is that UM was scoring a point every 13.03 yards in 2009. Despte that, UM is on pace to score 78 more points this season than last. Go figure.
What does this mean moving forward?
It all has to be positive. I mean, the DEFENSE HAD THEIR BEST STATISTICAL GAME OF THE SEASON!!! That alone is reason to celebrate. On top of that, UMs offense had their best game as well. If UM can limit their turnovers the rest of the season, or they can create some of defense, then I feel comfortable saying that there isn't a team on UMs remaining schedule that they can't beat.
Anyway, let's move on to week 9. Game at PSU...
Charts first? Yes... CHARTS
Well, with the bye week, UMs stats did not change. However, PSU was able to help their cause a little bit. They put up their second highest point total on their fourth lowest yardage total. That increased the predicted point total by 1 point.
UM still remains a favorite to outgain their opponent in all of their remaining games besides OSU. They are a -113 yard dog to OSU. However, UM was a 48 yard dog to Iowa and out gained them by 139 yards, for a 187 yard turnaround, so anything is possible.
UM - 527 yards
PSU - 369 yards
Score Predictor based on statistics...
UM - 35
PSU - 24
My score predictor based on gut feeling and blue shades - UM 42-17
I just took the prediction down the next avenue. I've calculated a percentage error of my predictions and then formulated a high and low for yardage output and their corresponding point totals.
Why Rich Rod And Other Coaches Don't (And Shouldn't) Always Make Decisions Based On Those Statistical Percentages – And A Proposed Decision Table Coaches Could Use
Before beginning, let me state that (to the best of my knowledge) the statistical calculations that all of these analyzes use are absolutely correct. However, further review and some additional analysis reveals there are very good reasons coaches do not (and should not) always use the statistical percentages to make decisions.
Synopsis: Using probability and statistics, several different analyzes have concluded that football teams should Go For It far more often on 4th down (even when they are in their own end of the field). However, virtually no football coach (at the college or pro level) even comes close to following this scientific decision-making criteria. So, the obvious question is, "Why don't coaches make decisions during a game based on the statistical percentages?" There are three shortcomings of using the statistical analysis:
- The statistical analyzes set decision thresholds that are too precise and too low.
- Using statistical analysis to make decisions about a very few events within a football game is mathematical fallacy (especially at the precision proposed).
- The application of expected value to make decisions in football is problematic.
Therefore, to compensate for these shortcomings, decision thresholds that are less precise and significantly higher should be used.
Revised Decision Threshold Table: The analyzes that have been documented to date have not included a decision table showing the magnitude of the net advantage used to make recommendations. Here is a decision table that shows the magnitude of the net advantage and uses less precise and significantly higher thresholds. While this significantly reduces when a team should Go For It on 4th down, the results are more credible, more likely to be followed in actual games, and more likely to benefit the offense.
In the table, ONLY the 10 events in the larger blue font in the light blue shaded cells (the upper right hand corner of the table) are recommended to Go For It on 4th down. They are:
- Fourth and 1 yard to go from your own 40 yard line to the 50 yard line
- Fourth and 3 yards to go (or less) from the opponents 49 yard line to the opponents 40 yard line
- Fourth and 5 yards to go (or less) from the opponents 39 yard line to the opponents 30 yard line
The recommendation is to Go For It under these criteria when the score is relatively close and time is not a factor. Specific game conditions (such as weather conditions, earlier possessions during the game, current score, time remaining, confidence in having a play that will get the first down, etc.) may provide insight to modify the recommendations. I stopped at the opponent's 30 yard line because inside the 30 yard line, field goals become reasonable for most college football teams. In college football, teams have significantly different field goal success rates and any analysis should be based on that team's anticipated field goal success rate.
Comments on the Decision Table: Key points to consider when reviewing the decision table are:
- I deliberately smoothed the data, looked only at 10 yard increments, and rounded the results to reflect the margin of error inherent in the analysis.
- If the decision threshold is set as breakeven (+0.00), comparisons to other analyzes such as The Mathlete indicate a high level of consistency. (BTW, the red (0.0) numbers in the table are actually very small negative numbers rounded to (0.0). The black 0.0 numbers are very small positive numbers rounded to +0.0)
- I took the numbers in the table and calculated one standard deviation (not sure how valid this is). One standard deviation is 0.9 and I decided to use 1.00 as the threshold. So, the threshold can be described as, "Go For It whenever your expected points are 1.00 or greater".
- Glass half full or half empty? Take a look at the values in the table. Except for the positive values in the upper-right hand corner, and the negative values in the lower right hand corner, the others are very close to 0.00. From a conservative standpoint (half empty), the conclusion is to only Go For It in a limited number of cases. From an aggressive standpoint (half full), the conclusion would be to almost always Go For It except for a limited number of cases.
- The "probability of scoring" was added to provide another view of the impact of decisions. For example, if you Go For It and make it from your own 40 yard line, you still only have a 32% chance of scoring (7 points). However, if you don't make it, the opponent gets the ball at your 40 yard line (i.e. Opp 40 in the table) and has a 46% chance to score. If you punt to the 20 yard line, the opponent has just a 20% chance of scoring. This relationship holds true for all the other yard lines – if you Go For It and do not make it, you more than double the opponent's chance of scoring (versus punting).
An Example of An Advantage of 1.1 Expected Points Per 4th Down Attempt:
Since it is obviously impossible to score 1.1 points, what does an advantage of 1.1 expected points mean? Well, it does not mean you are going to be successful and make the 1st down. Even if you make the 1st down, it does not mean you are going to score on this possession or that you will score before your opponent. It only means that over a large number of similar possessions over several games the net points you score divided by the number of 4th down attempts will equal 1.1 (each specific attempt may result in: turnover on downs, a punt, a FG, a missed FG, a TD, a subsequent turnover, etc.). Here is one possible scenario of what actual game results could look like with 4th down and 1 at your own 40 yard line:
This is, deliberately, a very simple scenario. It will likely take many more attempts than just 4 to approach the expected points in the table and there are many other possible scoring combinations. However, it does provide examples of how expected points translate to actual game results. Items to note in this example:
- The offense is at a net disadvantage until the fourth attempt.
- Net Offensive Points Per 4th Down Attempt (1.0) is close to the expected points per attempt (1.1) in the decision table.
- Total Offensive Points Per Possession (2.3) is close to the expected points for an offense from its own 40 yard line (2.2).
- Total Opponent Points Per Possession (3.0) is close to the expected points for a drive starting from the opponents 40 yard line (3.2).
This example also illustrates the significant risks involved in the decision: if the game ends prior to the 4th attempt, the offense is at a net disadvantage of 3 points and the result may be that you lose the game (even though the expected point analysis does not directly state this as even a possibility).
Development of the Proposed Decision Table: The decision table was derived from two basic sources: Football Outsiders Figure 1: Offensive Efficiency From Field Position and the Mathlete Never Punt With Denard? Fourth Down Strategy Revisited. I used the FO table (shown below) to calculate Expected Offensive Points by field position and the Mathlete's table for 4th down conversion rates. Note that many of the data points I used are not the exact numbers from these two sources. I smoothed the actual data to eliminate some minor anomalies (BTW, this does not affect the results – it is just a pet peeve with me).
The results in the decision table appear to be reasonable based on a comparison to these two sources as well as the Advanced NFL Stats When Should We Go For It On 4th Down? and David Romer, "Do Firms Maximize? Evidence From Professional Football", 2005. I did not use dynamic programming but used what I believe is a reasonable approximation. If anyone has a decision table with different or more accurate numbers, it would be great to compare and contrast the results. The decision table consists of:
Column 1: Yards To Go
Column 2: 4th Down Success Rate. This is based on The Mathlete's data for an average college football team. This introduces the first potential for a significant margin of error. Even if this was the actual success rate for a specific team over the first 10 games of the current season, does anyone believe it is the exact success rate for the 11th game? Of course not. But this does not completely invalidate the analysis. It does mean the decision should use less precise with higher thresholds.
Yellow Row At Bottom of Table: Starting Field Position (on the 4th down play).
Light Blue Row At Bottom of Table: Expected Points Per Offensive Possession (from that field position)
Orange Row At Bottom of Table: Probability of Scoring (7 Points) This is (Expected Offensive Points / 7) and provided as a reference only – not used in the calculations.
Columns 3-9: Expected Offensive Points (EP) Per 4th Down Attempt of Decision. This is based on the probability of making the first down, the starting field position, the expected offensive points from each specific field position, and the average net punting distance. The decision table provides the net expected offensive points per 4th down attempt of Going For It versus Punting. A positive number indicates a net advantage for the offense and a negative number indicates a net advantage to the opponent. I'll use a team's own 40 yard line with 4th down and 1 as the example.
EP of Decision To Go For It = EP (Make It) + EP (Fail)
Expected Offensive Points of Making It on 4th Down Is straightforward:
EP (Make It) = Probability of Making It X Expected Points At This Field Position = 72% X 2.2 = 1.58
Expected Points of Failing to Make It on 4th Down is obviously negative but also a bit tricky. You are still going to give the opponent the ball if you decide to punt rather than Go For It. So, the opponent would have some expected points anyway but based on a different field position. Therefore, I use the NET Expected Points in the calculation:
Net Expected Points = Expected Points After Failure To Convert – Expected Points After Punt
Expected Points After Failure To Convert = (3.2) Points (they are now on your 40 not their own 40)
Field Position After Punt = their own 20 yard line
Expected Points After Punt = (1.4) Points (they are now on their own 20)
Net Expected Points = (3.2) – (1.4) = (1.8) Points
EP (Fail) = Probability of Failing To Make It On 4th Down X NET Expected Points = 28% X (1.8) = (0.50)
EP of Decision To Go For It = EP (Make It) + EP (Fail) = 1.58 + (0.50) = 1.08
Background: The folks at Football Outsiders analyze college football using two systems (FEI and S&P+), Advanced NFL Stats provides analysis of pro football, the Mathlete has his analysis, and I am sure there are several others. The claim to fame for most of these systems is that a computer can take advantage of a statistical analysis of huge amounts of data: "nearly 20,000 possessions every season in FBS college football" or "every play of all 800+ of a season's FBS college football games (140,000 plays)", etc. A computer analysis is required because the human brain is simply incapable of processing this amount of data.
In addition to the primary result of ranking college football teams, these systems provide other analysis such as Never Punt With Denard? Fourth Down Strategy Revisited, the success rate of scoring in college football from every starting position on the field, or When Should We Go For It On 4th Down?
Here is the FO table that I used to calculate Expected Offensive Points by field position.
The Statistical Analyzes Set Decision Thresholds That Are Too Precise and Too Low: The decision threshold for all of these analyzes appear to have been set at breakeven (+0.00). This ignores the inherent margin of error and assumes a coach should take significant risks even when the rewards are essentially zero. (One example is the recommendation that teams should Go For It on 4th and 1 from their own 15 yard line!) The result is a loss of credibility in the analysis and a reluctance to believe and/or follow any of the recommendations. Here are three examples of the recommendations of when to Go For It on 4th down. The first is from the Mathlete:
The second is from Advanced NFL Stats:
The third is from the seminal investigation of the choice in football between kicking and trying for a first down on fourth down, David Romer, "Do Firms Maximize? Evidence From Professional Football", 2005.
Notice that all three of these analyzes recommend that a team should Go For It on 4th and 1 (Mathlete) or even 4th and 2 (Advanced NFL Stats and Romer) from your own 10-13 yard line! The reason? All three of these use the very precise and very low criteria that any value above 0.00 is an advantage to the offense and, therefore, warrants going for it on 4th down. This would be analogous to ticketing everyone that is going 0.01 miles over the speed limit – technically correct but impractical in the real world. IMO, anyone presented with the recommendation, "Go For It with 4th and 1 yard every time on your own 13 yard line" would be in disbelief and would dismiss any and all other recommendations from the same analysis.
In addition, the end result of these decisions is that "This evidence suggests that a rough estimate of the potential gains from going for it more often on fourth downs over the whole game is …an increase of about 2.1 percentage points in the probability of winning." (David Romer, "Do Firms Maximize? Evidence From Professional Football" 2005, Page 28). With a 12 game college football season, this corresponds to just one additional win every four seasons! Thus, you would expect a coach to Go For It on 4th down in hundreds of different scenarios (depending on field position, yards to go, expected conversion rates, expected net punting distance, expected field goal distance, game circumstances, etc.) on the prediction that every 4 years the team will win one extra game.
Using statistical analysis to make decisions about a very few events within a football game is mathematical fallacy (especially at the precision proposed): It is somewhat ironic that the advantages gained through the statistical analysis of tens of thousands (or hundreds of thousands) of data points is, in fact, why the results are not, can not, and should not be used to make decisions during a football game. In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.
The law of averages is a term used to express a belief that outcomes of a random event will "even out" within a small sample. As invoked in everyday life, the "law" usually reflects bad statistics or wishful thinking rather than any mathematical principle. While the law of of large numbers does reflect that a random variable will reflect its underlying probability over a very large sample, the law of averages typically assumes that unnatural short-term "balance" must occur.
The 4th down analysis relies on the law of averages and not the law of large numbers. Decisions based on the law of averages (also called the gambler's fallacy) are a recipe for failure.
One of the critical inputs of 4th down analysis is the conversion rate that is anticipated on 4th down for various yards to go. Because there are so few 4th down attempts, all of the analyzes use 3rd down conversion rates instead. Let's assume that the anticipated conversion rate on 4th and 1 yard to go is 75% and that this is based on actual data from thousands of 3rd down and 1 attempts. The theory of large numbers predicts that, over a large number of 4th and 1 attempts, a team should expect that 3 out of every 4 attempts of 4th and 1 will be successful. Unfortunately, there are likely to be only a very few 4th and 1 attempts in a single football game – often less than a total of 4. If you have just one 4th down and 1, it is either successful or unsuccessful. If successful, the result will be better for the offense than the expected value in the analysis (hooray!). If unsuccessful, the result may be that you lose the game even though the expected value analysis does not directly state this as a possibility (it may be stated in a footnote – it may not).
The application of expected value to make decisions in football is problematic. The concept of expected value originated in the 17th century, was defined explicitly in 1814 by Pierre-Simon Laplace, and is used extensively in probability and statistics. However, the expected value is only a theoretical value and may be unlikely or even impossible (such as having 2.5 children). Expected value is difficult to reconcile in football since the only possible outcome of a possession is 0, 3, or 7 points (ignoring safeties, missed PATs, or 2 points PATs). It is difficult to make decisions based upon "a net advantage of 1.1 expected points per 4th down attempt".
Our 2011 quarterback recruiting is still potentially not settled, but considering the current roster, the 2012 class looks to be the earliest that M would again be in the market for a long-term starter. Here's a look at some potential quarterbacks to keep an eye on in the 2012 class. These aren't necessarily prospects that Michigan is recruiting heavily, just names to keep an eye on.
|None||First 4 games of Junior season|
|Patti has thrown for 13 touchdowns to only 1 interception this year. He'll be visiting Michigan with his teammates for the Illinois game.|
|Ohio/Kenton||Ball State, Cincinnati, Missouri, Notre Dame||Sophomore Film|
|Mauk has a relationship with Brian Kelly at Notre Dame through his brother. His family has Michigan fans, but I think Notre Dame is in the driver's seat.|
|Hearing from Notre Dame, UCLA, Syracuse, Purdue, and Michigan. He wants to stay close to home, and has definite interest in Michigan.|
|Alabama, Arizona, Auburn, LSU, Oregon, TCU, Texas Tech, Others||First Game of Junior Season|
|Matt tore his ACL in his first game this season, but is going to be one of the best QBs in the country. He's interested in Michigan, and is hearing from them.|
|Teammate of '11 OL Chris Bryant and '12 OL Jordan Diamond.|
|Patrick is hearing from a lot of big time schools. Hasn't heard from Michigan yet, but is interested.|
|Former teammate of current Wolverine Cullen Christian.|
|None||Junior Season Opener|
|None||First 5 Games of Junior Season|
|Came to the Michigan/Iowa game. Teammates with '12 RB Will Mahone, and twin brother WR/DB Chris Davis. Versatile enough to play a few positions.|
Again, this is just a look at a few potential recruits. Michigan has obviously been in good contact with Nick Patti, and are in a favorable position as well. Matt Davis is an interesting recruit. He tore his ACL, but still has the top programs coming after him. Although he is from Texas he says he's wide open, and has definite interest in Michigan.
Welcome once again to the Ugly Game of the Week.
We're getting down to the end, I'm afraid, as there are only about seven winless teams out there in Division I, and none of them are in the same conference. So, the word of the week this week is "cupcake."
Penn State v Minnesota did not disappoint. Penn State was 2-10 on 3rd down, Minnesota outgained them by almost 100 yards, and yet Penn State won 33-21. I have no idea how. If you looked at the box score, you'd probably say Minnesota won, but they didn't. C'est la vie. This is not the week to pick on Minnesota; they're playing Ohio State (who just disemboweled Purdue), so go get 'em Gophers!
Speaking of that state, Ohio (NTO) clubbed Miami (NTM) 34-13; Miami did not help their cause with 6 turnovers. Key matchup: STOP THROWING TO THE WRONG TEAM! This game probably didn't ever make the ESPN ticker, so momentary confusion for Miami fans saying "Wait, didn't we already play them, and we're losing?" was averted.
Last, the ESPN headline says it best: "Sparse crowd watches Virginia drop E. Michigan." The EMUs were only down a field goal at half time, but their second-half drives were "punt, TD, punt, INT, punt, punt," whereas UVA scored on every drive of the second half. FTR Ron Enlish is now 1-19 in two years at that other Washtenaw County school.
Mmmm, cupcake mascots...
Speaking of mascots, there's some funny matchups this week, where top-ranked unbeatens go up against a variety of delectible baked goods. Numbers 1, 3, and 4 all play virtual nobodies. Sure, they're in-conference nobodies, but still nobodies.
s vs. s
Starting at the top we have Auburn playing Ole Miss. The Mississippi Rebels finally seem to have caught on to the fact that they lost the Civil War, meaning they're not so much rebels as loser traitors (fun fact: the University Grays, a regiment made up almost entirely of Ole Miss students, were the biggest losers of the entire war, sustaining 100-percent casualties in a single charge):
(Other schools named for warrior groups best known for getting absolutely trounced: Spartans, Trojans).
Since calling themselves Loser-Traitors was going out of vogue, Ole Miss students this year held an Internet contest for a new mascot, which the Internet being the Internet promptly elected Admiral Ackbar. So they chose to be a bear. Had they made Ackbar their mascot (or their head coach), maybe we could yell "It's a Trap!" here, but alas, Ye Ole Miss is #101 in points allowed, and they do have that glaring loss to Vanderbilt, um, glaring out from their schedule. Just hope that Auburn, who are the Tigers (yawn) doesn't get to play Nebraska in a bowl game, or the game may look like a tennis match on TV [Ed-M: except Nebraska apparently overcame their scoring aversion last week]. Auburn falling into the Number One Spot of Doom gets the "It's a TRAP!" award of the week.
s vs. s
Oregon, represented by Donald Duck and dressed by Huey, Dewey, and Louie after a Seattle coke bender, has an actual opponent-type-substance in USC, so they could/should move up to number 1 after pummeling the USC condoms (please please please).
s vs. s
Meanwhile, #3 Boise State plays Louisiana Tech in a game that features two blue and red teams playing on a blue field (I'm waiting for Kansas to get on the blue field bandwagon and paint a huge bird on it). The Broncos are one of seven teams with that name (10 if you include "Broncs" and "Bronchos"). That's pretty lame, but not nearly as lame as being one of 41 teams named "Bulldogs." LT's wins are against Grambling, Utah State and Idaho. They've lost to Hawaii and Southern Miss. Boise State just keeps rolling along with the #4 offense and #2 defense. Seriously, 12 points allowed per game? Bonus: the game is tonight.
s vs. s
Last, #4 TCU plays UNLV. UNLV has losses to both Idaho and Colorado State, and ranks in the 100s in offense and defense. Remember what I just said about Boise State's defense? Well, TCU is allowing 9 ppg. So, three FGs. Got it. As Wolverines, of course, we're all about rooting for the school with the better nickname, and here we have quite the contrast. A Horned Frog is something that might poison you if you step on it (up next: the Fightin' Sea Anemones?), though the last team to "step on" TCU was Boise State and they seem to be fine. The thing looks like a small, tailless Ankylosaurus. Fun fact: Phrynosoma, also known as a "horned toad," a "horny toad," or "horned frog," is actually neither a frog nor toad, but a type of lizard. Cue '80s rainbow thingy again:
As for UNLV, they're called the ... waitaminute ... Rebels? Were there even people living in Las Vegas during the Civil War? No, not really. Actually they took the name in the '50s because they thought of themselves as rebelling from Nevada. It's like Little Brother Syndrome, except a post-loss sin-fest is a hell of a lot more fun than burning sofas and tipping cows. Their mascot is "Hey, Reb." Their colors are "Scarlet and Gray," for the Confederacy. This is what happens when your naming committee just wants to get back to the craps table. Prediction: the Rebels get stabbed, poisoned, and turned into a gooey substance by the purple-wearing, horny-ass not-a-Frogs.
After the last two losses, there have been many opinions by many people on the question of whether the 2010 season is the same as the 2009 season
Those that say 2010 season = 2009 season point to the same record at this point, and that the wins and losses have come against the same teams as last year, and M will now play the same remaining teams as last year – in that same order
Those that say 2010 < 2009, argue that the losses this year have been worse, coming at home with wider margins
Those that maintain 2010 > 2009 say: “But look at all those yards the offense gained. We only lost because of turnovers”
This appears almost as tough as the P=NP (or P≠NP) question, except of course, everyone knows that P≠NP, but just can’t prove it (THE KNOWLEDGE, of course, has a simple proof for P≠NP that can be provided upon request)
All these things are not surprising
The most shocking thing to come out of the Iowa loss is that THE KNOWLEDGE’s overall season review is now not valid
it is an extermeley rare occasion when THE KNOWLEDGE does not soar
A highly irregular and undesirable change in the spatiotemporal continuum has caused the future to be slightly modified
The new future is a bit different, and THE KNOWLEDGE now knows about it
THE KNOWLEDGE has delivered on half of his review (BS Broncos in the NCG) by knocking off Alabama, OSU, LSU, OU, and Nebraska in the last few weeks. Other potential hurdles will be cleared in the coming weeks as well
However, since THE KNOWLEDGE’s words have not completely come to fruition, THE KNOWLEDGE announces his retirement from this season
THE KNOWLEDGE will re-appear in the offseason to describe the many coaching changes that are to occur in the Big Ten this year
THE KNOWLEDGE CHALLENGE may, however, continue if the masses still wish so
On that note, THE KNOWLEDGE would like to congratulate the following two and a half men for winning the 5th edition of the CHALLENGE and becoming Protégés of the week:
- Misopogon(half winner for predicting the score, but at a different site)
All of them predicted the score accurately!
As mentioned previously, association with THE KNOWLEDGE has improved the predictive capabilities of the masses
Adios, for a few months
During the summer it was suggested that I contribute some diaries about the golf teams. The fall has just finished up for the men’s team so I thought it would be a good time to give everyone an update.
To start, I wanted to offer an explanation of how college golf actually works for those who might not know - for the ones that do, I apologize for being redundant. The typical college golf tournament features 10 to 15 teams competing over the course of 54 holes with each team starting only 5 golfers. In each of the 3 rounds the team counts the best 4 scores of the 5 players. Here is a leaderboard from the last event that the men’s team played in: http://www.golfstatresults.com//public/leaderboards/team/static/team2068.html
As you can see, Michigan finished 4th with rounds of 279, 275 and 289 for a total of 843. If you click on “Michigan” it links to the 3rd round scorecards of each of the players. In this particular event, Miguel’s scores were dropped in each of the 3 rounds – shown by the chart at the top of the page.
Big Ten championships and NCAA Regionals/Finals are played in the spring for college golf. Similar to basketball, the winner of the Big Ten Championship is awarded a spot in NCAA Regionals. Besides the automatic qualifiers, to be selected for NCAA regionals a team must finish with a ranking around 60th or better in the nation and have a record of better than .500. Golf team’s do not play matches against individual teams throughout the year, instead all competition is conducted in the form of the tournaments described above. Going back to the tournament page I linked above, Michigan’s “record” is obtained by beating the teams in each tournament. Michigan was +7 in that tournament after beating 10 teams and losing to 3. The team ranking is currently 30th in the nation which is affected by their overall record as well as their record against teams in the Top 25/50.
In the 5 events in the fall, the men’s team compiled a record of 48-18-1, leaving them 30 wins above 500. After a slow start in the first event (9th), the team finished with 1 tournament victory and a total of 4 top-5 finishes.
One of the most representative stats for a college golf team is the number of Top-20 individual finishes for a team. To win tournaments you do not have to have the individual champion, but rather 3 or 4 solid performances. As a team Michigan had 11 Top-20 finishes this fall and 4 of those came during the team victory in the Windon Memorial Classic.
The fall season was solid, not spectacular, but solid. The foundation of this team is based on 3 guys: Lion Kim (Sr), Matt Thompson (Jr) and Jack Schultz (So). Lion came off a solid summer which included a win at the 2010 U.S. Amateur Public Links and earned his first individual tournament win of his college career ( http://mgoblog.com/mgoboard/um-golf-lion-kim-wins-us-amateur-pub-links ). Matt has maintained his solid play since he was part of the 3rd place '08/09 team as a freshman. And Jack, after earning himself Big Ten Freshman of the year in 2009, has been playing well this fall too.
At the beginning of the year I would have said Joey Garber (Fr) and Alexander Sitompul (Sr) were going to be the wild cards this year. That has proven pretty accurate as Sitompul went from leading the team in the first event to not starting the last two events of the fall. I had the opportunity to play with him for two years and I can honestly say I have never seen anyone with more potential. He is a physical freak and mentally equipped for pressure but he hasn’t been able to consistently contribute. Joey, on the other hand, has lived up to his recruiting hype (http://mgoblog.com/mgoboard/um-golf-commit-joey-garber-wins-michigan-amateur ) and has been impressive since his first event including a 2nd place finish in the Bank of Tennesse Intercollegiate.
The issue that could really hurt this team going forward is depth. Lion, Matt, Jack and Joey have pretty much solidified their spots in the first 4 positions but there is a hole in the 5 spot. The most likely person to fill that hole by the end of the year is Alexander Sitompul. He is experienced and when he is playing well he is the best player on the team. Rahul Bakshi (So) and Miguel Echavarria (So) will also be in the mix throughout the spring but the team is thus vulnerable if one of the top 4 fall to injury, sickness, ineligibility, or just a slump. None of which are extremely likely, but it is not out of the realm of possibility and has happened before.
The golf team is now in the offseason until returning February 11th for the Big Ten Match Play tournament in Florida.