frank beamer #1
A closer look at age of first exposure to football and later-life cognitive impairment in NFL players
This is in part a response to a thread a couple weeks back where I posted a link to the new study by Robert Stern and Julie Stamm et al in the journal Neurology that shows an apparent causal relationship between age of first exposure (AFE) to football and cognitive impairment in NFL players. The comments to the original thread hit a full spectrum that can be honed by a close reading of the study and past work on CTE. A few things have happened since that post that make this more interesting; the Super Bowl for one, John O’Korn transferring into Michigan for another and a civil lawsuit filed against the national office of Pop Warner football for the wrongful death of Joseph Cernach. I’m going to take a closer look at the actual data from the new study to refute some points made in the previous thread (including my own). Then, I’m going to apply this new data to previous studies and finally I will present my opinion as to where these and other recent events are leading.
First the latest CTE news story in which a suit has been brought in a federal court in Wisconsin against The Pop Warner Foundation stating that Pop Warner failed: to train coaches, to use safe helmets, to limit contact in practice, to teach players to use safety equipment and finally, failed to follow established concussion protocols dating back to 1997. The suit was filed by Debra Pyka, mother to Joseph Chernach who suffered from CTE having been diagnosed after he committed suicide at the age of 25 in June of 2012. Joseph played Pop Warner from the age of 11 in 1997 to 14 in 2000.
This is Joseph (a Michigan fan) with mom on the left and Joseph in happier times on the right. Photos are taken from Joseph's donor page at the Sports Legacy Institute (SLI) website and from photos supplied by the family to news outlets.
Fixed tissues from Joseph were examined by Ann McKee at the Boston University Center for the study of Traumatic Encephalopathy (CSTE.) His case was classified as stage 2 - possibly stage 3 and remarked as one of the worst for his age. Joseph’s complete brain was evidently not sent, preventing definitive staging.
This wasn’t the only suit filed in the last weeks but it got my attention and brought back the issues put forth by the CSTE study that came out on Jan 28th. It is important to grasp what that study is saying if only because we are likely to get many more lawsuits like the one above in the near term as well as a continued flight from youth football (participation already being down 29% since 2008.) But these are not the only reasons.
The study in question is entitled
Age of first exposure to football and later-life cognitive impairment in former NFL players
If you haven’t read it and are able to do so… just do it. It’s not that technical. Pundits in the media, however, and others continue to misconstrue its conclusions and validity which is another reason for this diary. What follows here is my understanding of the data presented with sincerity if not authority. It’s clear from the comments to the original board post that many were interested in the work but didn’t have the time to research the journal article.
The study is a cross sectional analysis for causal factors to explain actual cognitive impairment found in a sample of NFL players. The researchers pulled only from those players who were known already to exhibit cognitive, behavioral or mood symptoms in the 6 month period prior to participating. That is what a cross-sectional study is – a cross section of a population. There is no control group. A control is not needed for this sort of examination.
The subjects were pulled from a group of about 150 players who were vetted so as not to have any previous central nervous system (CNS)disorders (no Parkinson's, Alzheimer's, epilepsy or any other incoming disorder.) That cuts out quite a few.
The remaining subjects were then paired by similar age and different AFE to FB (one <12 the other >=12.) Current 10 year old (y.o.) FB players get different coaching and different equipment than 50 y.o. ex-FB players did 40 years ago. This pairing controls for the era of football – as the game has evolved year to year since it was first played but specifically in the living history of the NFL players in the study. The older players did not have as much opportunity to play youth football which further limited the possible pairs.
After all the selection is done only 42 players remained in the study population, 21 in each AFE group. There has been much talk about what exactly you can determine from a sample size of only 42 players. Well it turns out you can do quite a bit. Below is the breakdown of the demographics in the study taken from the article.
Looking over the demographics AFE to FB is the primary discriminate along with lesser but significant difference in duration of play(DOP). The confounding nature of DOP and AFE is a valid caveat to any conclusion drawn from this cross section of subjects. Maybe instead of the AFE it is the total number of hits taken that determines later life cognitive impairment. DOP (and age – which is not confounded due to the paired experiment design) was however accounted for and adjusted for in the analysis to focus on AFE.
Three tests were chosen for the analysis (given their focus on the theoretical cognitive deficits expected in CTE) and these were summarized in 9 scores. These are below in unadjusted and adjusted form in the exact data tables published in the journal Neurology.
All the tests are significantly lower for the AFE <12 group. While any significance is interesting, all of the measured outcomes being significant and lower is even more so. Yes, there are only 21 players in each group, but the significance of each of these scores is very high. Suppose you flipped a coin nine times and it came up heads all nine times… you would look at the other side of the coin wouldn’t you? Suppose it came up heads 189 times. That is all this preliminary study is saying. Youth football is a factor in the type of cognitive impairment associated with CTE in NFL players.
In retrospect this confirms a previous study on CTE in December of 2012 done by Ann McKee and Robert Cantu et al though the age of first exposure to the repetitive head injury was not suggested there. Let me suggest that now. That study was appropriately entitled
The spectrum of disease in chronic traumatic encephalopathy
The study included 85 recently deceased subjects known to have suffered repetitive mild traumatic brain injury (mTBI) along with a control group of 17 subjects with no history of the mTBI. The brains of these subjects were donated to the study for neuropathological evaluation along with an independent and blind parallel series of post mortem interviews with next of kin to determine case history.
68 of the 85 subjects showed signs of CTE(80%), while 51 of the 85 subjects were diagnosed with CTE exclusively(60%.)
From the pathology a staging system is laid out to which the behavior and historical data are spliced. Part of this historical data is the age at time of death. I took the liberty to put that into an excel table below.
Here is the same data graphically represented next to the iconic images of the staging done by Ann McKee in this study.
What hadn’t occurred to me (and isn’t done in McKee’s analysis either for that matter) was to take this age at time of death data and extrapolate back to stage 0 which given the progressive model for the disease would be the time the CTE started.
Here’s the same data with a linear regression.
Admittedly this is an N of 51 and only 33 of these are NFL players. The implication, however, is that CTE started at age 11 and 3/4 years old on average. This is a possible reason for the new study in the first place.
All the studies are calling out for more longitudinal designs to be funded and carried out. That would be about right if two sorts of people were doing the calling out. One would be the scientific sort who are careful with their claims and mindful of their funding. The other sort would be the watchdogs of the sport. That would be the NFL executives and owners.
For the rest of us these cross sectional studies will do just fine. There is no way I would ever ever let my son play the game of football as long as he was a minor in my charge. You don’t need broad based studies to find cause. It wouldn’t take too much convincing if he showed resistance.
There is obviously much more to these studies than I’m relating here. I encourage you to look for yourself to ferret out the details you might be interested in.
Cherry picking studies and data from science journals is a good way to get off base and picked off on a college football blog. I do want to present this table however, again from McKee and Cantu’s spectrum snapshot in 2012.
This is tying together the case histories (gathered by Robert Stern who is another author of the Spectrum paper.) From this chart you can begin to get a clinical take on what the progression of this disease is like. I’m showing this because there were some people who responded with either denial, disdain or ignorance to the dangers here.
This is from the FAQ at BU CSTE
The symptoms of CTE include memory loss, confusion, impaired judgment, impulse control problems, aggression, depression, anxiety, suicidality, parkinsonism, and, eventually, progressive dementia. These symptoms often begin years or even decades after the last brain trauma or end of active athletic involvement.
Roger Goodell in the many interviews from Super Bowl week was happy to point out that hits to defenseless players are down 68% in 2014 (yes they track that), concussions were down 25% and that concussions in the past three years have dropped from 173 a season to 111. I doubt they track or could track the sub-concussive blows that are likely the true culprit in CTE.
Still we got the Edelman hit with 11 minutes to play in the 4th with no concussion protocol or independent review during or after the game. It’s going to be impossible to take the football out of football no matter how much you deflate it.
This has already gone too long. I’ll save you my thoughts on where we’re going from here. But I do think Harbaugh took O’Korn and Oregon took Adams for reasons that aren’t entirely unrelated. You can’t have enough QBs in the games to come.
Here is a quick little diary in response to John U Bacon's article today and much of the commentary on his site and this one. I don’t agree with Bacon’s broadside on Brandon mostly because it is not needed to explain poor ticket sales. Do people come to games when the team doesn’t win? No they don’t for the most part. At least that is what I take to be common sense. Reading Bacon’s article and the comments on it…most people are thinking there is more than winning and losing driving attendance at Michigan and some would expand this to CFB in general. This diary is a first step in understanding the relationship between winning and getting people to show up on game day.
I have taken the attendance stats, data and pictures below from Bentley’s web article on Michigan stadium ,additional data from CFBstats for recent years and other data from the Michigan Stadium wiki site.
The attendance data at Michigan stadium is reported out back to the inaugural year of 1927. I don’t see a downloadable, consistent or comparable dataset from the Fairgrounds, Regents or Ferry field, but there are several mentions of sold out games and ticket lotteries in the Ferry field days of 1906-1926.
I take the building of Michigan stadium to be a statement of near capacity seating in Ferry Field at least. In reading through the history of Regents Field in the period of 1893-1905 (again from the Bentley site) it would seen the final games there were also near or over sold. Capacity at Regents was apparently 15,000 in 1905.
Ferry Field finished with a capacity of 45,000 which makes this Wisconsin game in 1924 oversold as well.
I wish I had the data on these two fields but I don’t think it’s a stretch to assume near capacity seating for some of the games and seasons especially in the later years.
Once Michigan stadium gets built however capacity seating is not the rule until the 1970’s. That’s forty plus years of data showing wins vs. attendance. Here is that data broken out %Capacity vs. the Win% (taken straight up and in rolling 3, 5 and 10 year windows.) The W/L data is bolded pre Michigan Stadium (1883-1926).
Using Percent Seating Capacity for attendance is misleading as the capacity of the Michigan stadium is increasing over this period. The % capacity however is just the record that is at issue with this season’s ticket sales and I would imagine the AD’s marketing plan. Here is the build out in seating capacity over the course of the stadium’s history.
Michigan Stadium Capacity
What is of interest hear is the correlation of Ws to the attendance. Initially this was fairly noisy but when I took the rolling 10yr Winning % the correlation smoothed out.
Despite a supposed excellent Ufer enthused and storied cheap game day experience throughout the post Tom Harmon days, and even before in the brand new stadium – when wins were down long term (in a ten year window at least) sales suffered. It looks to me like common sense prevails here. W and Ls are sufficient to explain attendance.
There were a couple comments that the Michigan situation mirrors that of other programs and CFB in general. I pulled that data as well but I’m not going to look at it here – if ever. I strongly feel like the Michigan experience is different than other programs like Alabama, Texas and even Ohio State in history and the present day. All that said there are common trends affecting us all.
The growing cost of an undergraduate education is frightening and somewhat disheartening to me as the parent of a newly teenage child. I don’t blame students for staying away from a game day experience when the stakes are higher than ever to succeed in class and in life. When the team comes back above the ten year mean in wins or even sooner – I think they will find a way to sit with their friends or some other Wolverine will find their way to the now barren end zone.
My apologies for data misconstrued or otherwise. It’s a diary… not a white paper.
It turns out Michigan led the nation in adjusted open field rushing yards (AOFY) in 2012. What I mean by that is whenever a team got their RB 10 yards past the line of scrimmage on any given play – Michigan on average had more added yards than any other team in the nation. Unfortunately Michigan wasn’t very good at getting ball carriers 10 yards past the LOS. Here’s the run down for the entire FBS up until the bowls.
Adjusted Open Field Yards - FBS 2012
|Team||Conf||AOFY||AOFY||AOFY Rate||AOFY Rate|
|San Diego State||MWC||12.51||9||12.63%||9|
|Michigan State||Big Ten||11.06||25||8.10%||117|
|North Texas||Sun Belt||11.00||26||9.86%||109|
|Middle Tennessee||Sun Belt||10.44||34||12.01%||101|
|San Jose State||WAC||10.39||35||7.80%||120|
|Florida International||Sun Belt||10.27||37||10.84%||91|
|Western Kentucky||Sun Belt||9.90||50||14.04%||83|
|West Virginia||Big 12||9.65||53||16.80%||10|
|Oklahoma State||Big 12||9.41||57||13.82%||18|
|Arkansas State||Sun Belt||9.22||62||13.36%||8|
|Ohio State||Big Ten||9.09||66||17.55%||7|
|South Alabama||Sun Belt||8.77||76||8.41%||90|
|South Florida||Big East||8.33||82||12.73%||57|
|Florida Atlantic||Sun Belt||8.10||89||8.75%||113|
|Texas Tech||Big 12||7.81||96||15.38%||17|
|New Mexico State||WAC||7.41||103||9.32%||95|
|Penn State||Big Ten||7.09||105||7.05%||108|
|Iowa State||Big 12||6.68||110||13.12%||89|
|Kansas State||Big 12||6.53||114||14.12%||43|
|North Carolina State||ACC||5.56||121||7.42%||116|
These are conditionally formatted – Blue to Red with the hue indicating the spread of these numbers. Michigan is the clear leader in this contrived stat.
To see Michigan leading the nation in any offensive stat was a surprise to me – and I thought I’d share it. It’s not an official stat by any means but it’s one that I came upon while looking to quantify Offensive Line (OL) performance. What I wanted to see was an offensive line performance stat/summary for 2012 based on the metrics Football Outsiders (FO) uses for the NFL.
What I’m finding is not what I wanted with respect to OL work but I’ll share some of that since it explains the table above.
Scheme is by far the more telling factor in rushing success in the FBS than NFL caliber OL talent or all-American status. Triple option teams do extremely well but without the boss hogs or broad reach blocking lineman of the primo run spread teams that I expected to dominate these stats. I don’t want to take anything away from any of these teams however. What they have done in rushing stats – doesn’t happen if the OL is not playing like a team.
Here’s the standard rushing yards per game with some minor tweaks. There are interesting differences between this and the OL stats I pulled and present later on…
Standard Rush Stats 2012
(minus sacks and FCS games)
|Team||Conf||% Rush Plays||% Pass Plays||Rush /G||Rush Yds/G||Rush Yds /G||Rush||Rush|
|Ohio State||Big Ten||63.32%||36.68%||257.67||42||10||5.83||8.54|
|San Diego State||MWC||61.53%||38.47%||239.73||59||14||5.65||9.15|
|Arkansas State||Sun Belt||53.45%||46.55%||211.27||20||30||5.35||7.59|
|Oklahoma State||Big 12||49.59%||50.41%||204.27||5||33||5.26||7.86|
|West Virginia||Big 12||44.64%||55.36%||195.73||8||41||5.56||8.97|
|Western Kentucky||Sun Belt||57.36%||42.64%||195.00||84||43||5.19||7.59|
|Kansas State||Big 12||62.27%||37.73%||194.45||62||45||4.87||6.22|
|Middle Tennessee||Sun Belt||58.20%||41.80%||189.91||68||48||4.82||8.28|
|South Florida||Big East||48.77%||51.23%||167.55||91||62||4.89||7.22|
|Florida International||Sun Belt||52.68%||47.32%||163.17||71||68||4.33||7.65|
|Michigan State||Big Ten||48.59%||51.41%||162.75||90||70||4.52||6.23|
|Penn State||Big Ten||48.76%||51.24%||159.33||46||74||4.21||4.85|
|North Texas||Sun Belt||53.89%||46.11%||159.27||87||75||4.21||8.14|
|Iowa State||Big 12||48.17%||51.83%||153.64||97||82||4.44||6.31|
|Texas Tech||Big 12||37.64%||62.36%||147.73||10||91||5.21||7.51|
|South Alabama||Sun Belt||48.09%||51.91%||143.50||104||96||4.14||6.29|
|San Jose State||WAC||46.38%||53.62%||132.00||27||106||4.04||6.77|
|North Carolina State||ACC||40.99%||59.01%||121.36||48||114||3.67||4.96|
|New Mexico State||WAC||42.66%||57.34%||120.45||107||115||4.26||5.83|
|Florida Atlantic||Sun Belt||45.01%||54.99%||118.18||100||116||3.79||6.13|
I formatted standard deviation in green – because I’m not sure what’s good in that regard. I would say looking at the data however that a reasonably high spread in general is a sign of success. Std Dev is a tell for scheme and some of the OL stats I was breaking out when I came to Open Field Yards.
FO has done some good stuff with respect to offensive line performance. They contrived a few ways to tweeze out relative OL performance. Curiously I couldn’t find these methods applied to college ball. I gave it a quick whack in the first diary and came up with a gross reality check that pretty much matched my gut – OL performance was not good in that game and adjusted line yards were significantly lower.
Check the FO link and previous diary to define Adjusted Line Yards but here is a quick chart and definition of their derived stats for Adjusted Line Yards (ALY), Second Level Yards (SLY) and Open Field Yards (OFY) as compiled by FO.
What’s going on here is they are taking yards per play and giving them value based on the outcome. The concept is simple – the initial yards are more relevant to OL performance. The second level yards less so. The open field yards…not so much.
Adjusted Line Yards (<=10 yards)– are conceptually on the offensive line – no block no gain
Losses: 120% value – because if you don’t block at all that’s a TFL
0-4 Yards: 100% value
5-10 Yards: 50% value
11+ Yards: 0% value e.g. 10 yard gain is worth 7 yards… 20 yard gain is worth 7 yards…
Second Level Yards (6-10 yard gains) – are a combined ball carrier and OL stat
Losses – 5 yards: 0% value
6-10 Yards: 100% value e.g. 6 yard gain is worth 1 SLY…
11+ Yards: 0% value e.g. 10 yard gain is worth 5 SLYs… 20 yard gain is worth 5 SLYs…
Open Field Yards (11+) = are conceptually on the ball carrier
Losses – 10yards: 0% value
11+ Yards: 0% value e.g. 11 yard gain is worth 1 OFY … 20 yard gain is worth 10 OFYs…
- Caveat 1 - I took out Sacks.
- Caveat 2 – I took out games involving FCS teams. Which is a gift to Mich since we played UMass.
Caveat 3 – I include QB rushing here. I did this because it’s college and … well… duh… Denard. Scrambles don’t make much difference in the overall here and the NCAA counts them as rushes so … be it.
I looked at this data and tried to make sense of it. It didn’t look good for Michigan. So I did what any good MGoBlog diarist does and adjusted it to suit my thesis.
It still doesn’t look good for Michigan but this is what I did.
Adjusted Adjusted Line Yards AALY
- I normalized the losses over 10 yards to –10 yards. I did a sampling and these were snap issues (still an OL issue but not what I’m concerned about) – reverse plays gone wrong or mis-tagged sacks*. (*There’s plenty of errors in the cfbstats.com data BTW – but I don’t think they are significant. Most of them appear to be due to NCAA/Scoring issues anyway.)
Adjusted Second Level Yards ASLY
- I took out the plays that went for zero yards – in general you can’t hold it against the ball carriers if they didn’t get to the second level.
Adjusted Open Field Yards AOFY
- Same as above – I took out plays that went for less than 11 yards to isolate these from the mean OFY totals.
- With these adjustments in mind I added two columns for SLY rate and OFY rate to represent the % of plays that a team sprang ball carriers for these distances.
Here’s a revised chart with these adjustments…the distributions are better and the model is better for what these derivative stats are intended to represent.
I added an ASLY % rate to the summary table to show how often teams got their rusher to the second level as well as an AOFY % rate. These are important and significant changes over the FO yards stat. Their NFL rates of 2nd level and Open Field plays are rolled into their summary yards.
Since I had the data summarized I added the FO convention for Success Rate % for all rushing plays defined as follows:
- 1st downs that achieve 50% of yardage needed to convert or score
- 2nd downs that achieve 70% of yardage needed to convert or score
- 3rd/4th downs that convert or score
- Stuffed rate is defined as Percentage of runs where the running back is tackled at or behind the line of scrimmage. This includes QB runs minus sacks (this appears to be a press box discretion as there are negative QB runs that are not accounted as Sacks – I should look at this more closely but I don’t think it’s significant here.)
Finally I tallied Power Success straight up to the FO definition as percentage of runs on third or fourth down, two yards or less to go, that achieved a first down or touchdown. This also includes runs on first-and-goal or second-and-goal from the two-yard line or closer.
At this point I’m stultified by the challenge that OL statistical summarization presents wrt CFB (or any kind of football for that matter.) If I had time I’d follow the FO path and normalize this data to FBS averages… look at the individual ball carriers…take out garbage time… but I also kind of want to watch some bowl games and MSS wants me to take down the tree... and little TSS wants to ski. Let’s just say this is a work in progress.
Regardless of my problems… here’s the table that I was able to get up today…I added in Sack Rate (which I’m very skeptical of in terms of an OL criteria as I have seen previously when comparing the different SRs for Denard, Bellomy and Devin.) I went to three letter acronyms for the conferences. It is what it is.
OL Stats 2012 – FBS
|Team||Conf||AALY||ASLY||AOFY||Stuff Rate||ASLY Rate||AOFY Rate||Power Success Rate||Success Rate||Sack Rate|
|San Diego State||MWC||3.27||3.36||12.51||18.20%||36.40%||12.63%||8.35%||47.54%||10.62%|