One man’s take on the NFL’s safety issue

The NFL sent out this memo to its fans this morning. It’s too much to take.  Here, I dive in.

The NFL season is off to another exciting and competitive start.

Well, not quite. The Jaguars measure as the worst team through four games in recent NFL history, and the Broncos have been so good that Yahoo! contemplated what the point-spread were to be if they were to play Alabama.  Let’s move on.

As a league, we have an unwavering commitment to player health and making our game safer at all levels.

Notice the phrase used here (“we have”) instead of the the phrase which should’ve been used but can’t be used because it’d be a lie (“we have always had”)

We hope that our commitment to safety will set an example for all sports.

Yup. Hard not to see David Stern, Bud Selig, and Gary Bettman with their notebooks and #2 pencils out, truly impressed with how the NFL has handled things.

There have been numerous safety-related rules changes going back decades: from the 
1970s when we eliminated the head slap

That must’ve been a tough call. Although, to the NFL’s credit, baseball may still be having this issue. 

to the 80s when we eliminated clubbing

Pretty sure this isn’t out of the game. Just ask Jacoby Jones.

...to the 90s when we increased protection for defenseless players, to the 2000s when the horse collar 
tackle was made illegal.

The same 1990’s when you hired a rheumatologist to lead your concussion panel? And the same 2000s when that rheumatologist published bogus crap after bogus crap in Neurology?  Quite committed, NFL!

We will continue to find ways to protect players so they can enjoy longer careers on the field and 
healthier lives off the field.

Which of course is why you’re quitly pushing an 18-game schedule.

Recently, Hall of Fame coach John Madden, who co-chairs our Player Safety Advisory Committee, told me 
that players and coaches have truly adjusted to the new, safer rules. Coach Madden said the players 
are back to the fundamentals of blocking and tackling, using the shoulder rather than the head. As a 
result, the game is safer.

This is my favorite part. Like I read it and thought I had misread it. Who better to comment on NFL player safety than a 77 year-old who retired from the sport four years ago and probably watches a game a week from his television. Like someone in the NFL office said “you know what, we could really convince people that there’s nothing to see here if we get John Madden to say there’s nothing to see here. HE HAS A VIDEO GAME!!!”

We work closely with the NFL Players Association to ensure our players have access to the finest 
doctors and most cutting edge technology.

This must be a new practice. Again, the NFL investigated concussions as far back as the mid 1990’s. Unfortunately, their finest concussion doctor was a rheumatologist who was employed by an NFL organization (the Jets), and their investigation was entirely based on saving its own ass. Quoting the new book, “League of Denial,” the investigation dismissed the matter as a “pack journalism issue” and claimed that the NFL experienced “one concussion every three or four games,” which he said came out to 2.5 concussions for every “22,000 players engaged.”

We have supported youth concussion laws that have now been adopted in 49 states.

Two things here. First, obviously the league is going to support youth concussion policies. Not really that incredible. Second, what one state is behind the 8-ball here?

We have pledged more than $100 million to medical research over the next decade.

You pledged money already. But the research wasn’t impartial.

Including $30 million to the National Institutes of Health for independent research to advance the 
understanding of concussions.

All of the money should be going to independent research, not a third of it. Also, not a great time to be citing the NIH.

We have also embarked on a $60 million partnership with GE and Under Armour to accelerate the 
development of advanced diagnostic tools and protective materials for head injuries.

This research endeavor is surprising, because If the NFL was that serious about its helmets, the league could start by making players wear safest ones. Instead, it doesn’t.  Quoting from the New York Times, even as head injuries have become a major concern, the N.F.L. has neither mandated nor officially recommended the helmet models that have tested as the top performers in protecting against collisions believed to be linked to concussions. Some players choose a helmet based on how it looks on television, or they simply wear the brand they have been using their whole career, even if its technology is antiquated. 

That’s amazing. Players can choose their own helmets based on how they look, not based on safety. But hey, who cares, the league outlawed clubbing in the 1980’s!  And why worry about player safety when real safety DeAngelo Hall is sporting Lacoste during an interview!

The future of football is brighter, bigger, better, and more exciting than ever.

I’m not sure. One study in the Washington Post, cited a study which found an 11 percent decline in tackle football’s “core” participation the past three years.

For more information on our health and safety work, go to www.nflevolution.com

Love this website choice. For any fans of “The Office,” this reminds me of Dunder Mifflin Infinity: take the same name as the original product, and add on a fancy word (evolution or infinity)…it’s a can’t miss!

My conclusions? The NFL is covering its own negligence. I’d just appreciate it if the league flat-out admitted that it was wrong.

Why do I seem to care so much?  Well, for starters, I played four years of college football (this is me, a few pounds ago). Across football, and not just the NFL, players just went back in after getting concussions. You were kind of essentially considered to be a frisbee player if you didn’t. And I can’t help but think that if the league had properly conducted its research when in pretended to in the 1990’s, the game wouldn’t have had to wait to 2013 to invoke the rule and safety changes necessary to the game safer.

Moreover, I love the scientific research (or lack thereof) which is intertwined in league policy. The efforts the league made in the 90’s and 2000’s were embarrassing. From a statistics standpoint, their examples of “the players went back in, so there must be no long term concussion effects” are great for showing how not to do science. Why did the players go back in? Because they thought they had to!

Further, for all researchers, the negligence shown by the journal Radiology (for more info, click on this book excerpt…but the bottom line is that the journal wanted readership so it allowed terrible research to be published) and the bias shown by the NFL’s investigators, each of whom had a vested interest in proving that concussions were not causing long-term damage, is a reminder to us all about the importance of those conflict of interest forms that keep popping up, and that the peer-review process, while important, isn’t always perfect.

Do your RRRRR homework

 

 

One of my students tweeted this at me today.  I think it is awesome.  But I suppose this also means they aren’t paying attention to me.  But still, this is awesome.  So I guess it’s ok that they aren’t paying attention if they are creating brilliant images like this.

GregPirate

 

Argggghhhhhhh and Cheers.

Stat Pundit Rankings: MLB win over-unders

About two weeks ago, I used some familiar metrics to analyze how analytics-based websites performed as far as predicting MLB win totals. With the regular season now complete, winning bets have been cashed, and the official performance for each site is listed below:

O/U: The Hilton’s over/under for each team

BP: Baseball prospectus

TR: Team Rankings (caveat on the linked page: the site stresses their MLB predictions are a work in progress)

DP: Davenport

Zips: ZIPS projection system (espn.com)

PM: Prediction Machine

TB: Trading bases, an avid blogger and book-writer

Here are my metrics

MSE: Averaged squared error between the prediction and the win totals (lower is better)

MAE: Averaged absolute error between the prediction and the win totals (lower is better)

Corr: Correlation between the predicted and the win totals (higher is better)

Results

O/U BP TR DP Zips PM TB
MSE 82.65 74.40 98.60 85.43 87.53 94.27 71.53
MAE 7.37 7.33 8.40 7.37 7.40 7.86 6.93
Corr 0.66 0.70 0.58 0.64 0.64 0.59 0.71

Baseball Prospectus and Trading Bases appear to offer the only clear advantage over the Las Vegas line, at least among these predictions, as judged by a higher correlation and a lower MSE between observed and predicted values. On average, TB was the only prediction site to finish, on average, within seven wins of the actual results.

A savvy bettor would’ve finished 12-9 on bets where BP differed by the Las Vegas O/U by more than two wins, and 10-6 using the same cutoff for TB. Picks that BP and TB agreed (by more than 2 predicted wins) on finished 7-4

Here are the Vegas lines and each site’s picks. In some cases, the projected total wins might not add up to 82 per team, most likely due to rounding errors.

Team O/U BP TR DP Zips PM TB Actual
Diamondbacks 82.5 85 83 81 85 76.8 80 81
Braves 86.5 83 85 85 91 86.6 82 96
Orioles 78.5 75 81 75 82 79.2 76 85
Red Sox 82.5 85 79 85 84 80.5 83 97
Cubs 72.5 77 73 76 74 75.8 69 66
White Sox 80.5 76 83 76 80 85 78 63
Reds 90.5 92 84 86 90 91.1 84 90
Indians 78.5 80 74 79 80 76.8 85 92
Rockies 71.5 71 75 74 70 77.5 70 74
Tigers 92.5 91 86 95 91 89.7 95 93
Marlins 63.5 67 75 65 65 65.3 64 62
Astros 58.5 63 67 72 57 62.5 66 51
Royals 78.5 76 78 80 79 75 77 86
Angels 91.5 91 86 91 93 93.3 88 78
Dodgers 91.5 91 83 88 90 90.6 91 92
Brewers 81.5 78 83 78 81 77.6 78 74
Twins 68.5 65 74 69 66 70.9 66 66
Mets 75.5 80 78 76 66 76.8 74 74
Yankees 86.5 91 90 86 83 84.7 87 85
Athletics 84.5 83 86 84 78 85.3 85 96
Phillies 85.5 81 84 81 82 81 86 73
Pirates 77.5 80 77 81 77 74.8 79 94
Padres 73.5 76 78 76 73 72.7 81 76
Giants 87.5 85 85 92 87 85.1 88 76
Cardinals 82.5 85 86 83 85 85.1 90 97
Rays 86.5 87 88 86 88 89.5 93 91
Rangers 86.5 89 88 85 91 86.8 85 91
Blue Jays 88.5 84 78 86 94 87.5 82 74
Nationals 91.5 87 86 85 94 92.5 90 86
Mariners 77.5 78 79 73 74 74 78 71

For Eli Manning, 150 Games and Counting

 

 

Check out this graphic from the New York Times.  Eli Manning has now started 150 games in a row for the Giants.  That’s pretty hard to grasp.

Screen Shot 2013-09-30 at 2.55.56 PM

 

Cheers.

New England Symposium on Statistics in Sports (NESSIS) 2013


I attended the New England Symposium of Statistics in Sports (NESSIS) last Saturday at Harvard Science Center (See the sweet logo below) where I presented a poster.  The conference was organized by Mark Glickman and Scott Evans Scott Evans

NESSIS Logo

My poster (see below) was about openWAR, which is  a project I am working on with Ben Baumer and Shane Jensen.  Our goal is to create a completely open source version of wins above replacement (WAR) based entirely on publicly available data.  We’ve implemented openWAR in R and the package is currently available on github here: openWAR.  When we think it’s ready for primetime, we’ll be putting in on CRAN.

poster

I missed the first featured session because it was at 9:30am, and that’s not how I roll on Satudays.  During the parallel sessions at 11:30am, I decided to attend the non-NBA series of talks.  The first talk was by Robert Carver and he talked about R.A. Dickey and the curveball.  He was followed by Stephanie Kovalchik who gave an interesting talk about trends in tennis intensity.  She had a lot of really interesting data visualizations of tennis trends over the past few decades, but I can’t seem to find them online.  If anyone knows where I can find there, please point me in the right direction.  After her, Dennis Lock gave a talk about using random forests to estimate win probability.  At the end of the day I was trying to explain random forests to someone from ESPN (how awesome is that sentence), and I knew that random forests were essentially regression trees based on bootstrapped samples.  When I went to look this up to make sure I wasn’t lying about random forests, I found out that at each step the set of predictors in the regression tree is randomly chosen.  I did not realize this, but makes total sense.  Otherwise, the trees in the forest would all be very similar. So I learned something, and isn’t that the whole point of these conferences?

The final talk in this session was by Michael Pane who was attempting to cluster pitches based on pitch F/X data and improve classification of MLB pitches.  They call their procedure CLUMPD and they made a sweet interactive shiny app.  But I didn’t write down the URL,  and I can’t seem to find it by googling it.  Hopefully when they post the slides, the link will be in there.

Following the session I ate lunch with Ben Baumer, Mike Lopez, and one of Mike’s friends from UMass on the rocks outside of the Harvard science center.  After lunch I mean to go the the afternoon featured speaker, but I ended up talking to two San Francisco fans about my poster.  I asked them if they were presenting at the conference, and they told me that they didn’t even know the conference was going to be there.  They were just baseball fans in town to see a few Red Sox games and they apparently just stumbled across NESSIS and my poster.  After talking to the two guys from San Francisco, I talked to one of the members of the Tuft’s SABR club about openWAR for the rest of the time allotted for the featured speakers.  After we finished talking the actual poster session started at 3:30.  I met and spoke with a ton of interesting people.

Here’s a list of some of the interesting people that I talked to while at my poster:

  • Vince Gennaro – Author of Diamond Dollars: The Economics of Winning in Baseball, President of SABR, consultant to MLB teams, all around baseball fanatic
  • Eric Van – Former consultant for the Boston Red Sox
  • Michael Humphries – Author of “Wizardry: Baseball’s All-Time Greatest Fielders Revealed”
  • James O’ Malley – Professor at Dartmouth
  • Andy Andres – Teacher SABR 101 at Tufts
  • Doug Noe –  Professor at Miami (OH) (This was my favorite meeting because I had never met him before, but he told me that he really liked my blog and that I had actually written about him before.)

Right at the end of the poster session, Eric Van came over to my openWAR poster and criticized our definition of replacement player.  The way that we have defined it, about half of the players we have defined as being in the replacement group are below the average replacement player.  While I’m not sure that this isn’t ok technically, it’s a huge success for our larger idea.  By making openWAR completely transparent people are free to criticize, critique, and complement every single piece of our procedure (and we definitely welcome constructive criticism), rather than gues at what’s going on inside the black boxes of baseball reference and fan graphs WAR.

NESSIS then closed with a panel discussion.  The panel consisted of Ben Baumer, Eric Van, and Vince Gennaro.  The picture below is the panel, with Carl Morris (you know he’s a big deal cause he’s got a Wikipedia page) saying some words before the discussion began.  The panel was ultimately moderated by Andy Andres.

One of the interesting points the panel made was that in the beginning of SABRmetrics, a lot of the most interesting work was being done by fans and not necessarily the teams themselves.  This has entirely changed today due to the fact that baseball teams have access to mountains and mountains of data that are simply not available to the public or the public can’t afford.

Van also pointed out that the numbers don’t tell you everything.  You can’t just view numbers and ignore the personality of players.  For instance, if the numbers say that a guy should hit 6th instead of 2nd, you have to weigh the improvement your team will gain against the psychology of moving a guy from 2nd to 6th in the line-up.  In his words:

The numbers are just sign posts. You have to actually watch the game to see if you’re onto something. -Eric Van

The whole discussion was fantastic, and it was really interesting to hear the perspective of three people who have actually worked in baseball as statistical analysts.

BaumerPanel

Fantastic overall conference.  See you in 2015!

Cheers.

NCAA Football Top 25 – September 23, 2013

2013 NCAA Football Standings

Updated September 23, 2013

 
Rank Teams Record AP
1 OREGON 3-0 2
2 CLEMSON 3-0 3
3 MISSOURI 3-0 35
4 GEORGIA TECH 3-0 27
5 GEORGIA 2-1 9
6 WASHINGTON 3-0 16
7 FLORIDA STATE 3-0 8
8 ALABAMA 3-0 1
9 FLORIDA 2-1 20
10 OLE MISS 3-0 21
11 UTAH 3-1 NR
12 SO CAROLINA 2-1 12
13 ARIZONA 3-0 32
14 NORTHWESTERN 4-0 17
15 UCLA 3-0 13
16 NAVY 2-0 36
17 MIAMI-FLORIDA 3-0 15
18 USC 3-1 NR
19 COLORADO 2-0 NR
20 MARYLAND 4-0 28
21 OREGON STATE 3-1 NR
22 LOUISVILLE 4-0 7
23 OHIO STATE 4-0 4
24 STANFORD 3-0 5
25 MINNESOTA 4-0 NR

Full Rankings

Cheers.

MLB over-unders: Can anyone beat Las Vegas?

Back in March, several dozen websites, written by either professionals, bloggers, or, in some cases, professional bloggers, came out with predicted MLB win totals.

A predicted win total represents the number of wins this website or individual predicted for each major league team. These numbers can be easily compared to the Las Vegas line for each team (I used the one set by the Hilton) to determine if these predictions are worth our time, and, in some cases, our money.

Here are the sites I used:

O/U: The Hilton’s over/under for each team

BP: Baseball prospectus

TR: Team Rankings (caveat on the linked page: the site stresses their MLB predictions are a work in progress)

DP: Davenport

Zips: ZIPS projection system (espn.com)

PM: Prediction Machine

TB: Trading Bases, an avid blogger and book-writer

Here are my metrics

MSE: Averaged squared error between the prediction and the win totals*

MAE: Averaged absolute error between the prediction and the win totals*

Corr: Correlation between the predicted and the win totals*

*For win totals, I’m use each team’s estimated win totals from here (I’m too excited to wait until the end of the season!)

Results

O/U BP TR DP Zips PM TB
MSE 68.59 62.50 84.56 70.47 75.37 79.76 61.04
MAE 6.65 6.75 7.73 6.75 7.01 7.22 6.53
Corr 0.68 0.71 0.59 0.67 0.66 0.61 0.72

Baseball prospectus appears to offer the only clear advantage over the Las Vegas line, at least among these predictions, as judged by a higher correlation and a lower MSE between observed and predicted values. As for team rankings & prediction machine, their results were both disappointingly bad. (Note: Trading Bases came into the picture after the initial post, and also appears to be a clear winner). 

TeamRankings does offer this disclaimer about their projections:

A word of caution — while our preseason projections for other sports have proven to be useful indicators of where values may lie among the various full season futures bets, we’re not nearly as confident in our MLB preseason ratings. We’re publishing these in the interest of full disclosure, so that you know what the initial rating in our projection system was for each team. We’re most definitely not recommending that you use these ratings and forecasts to go place preseason bets.

Here’s the table of predicted wins for each site.

Team O/U BP TR DP Zips PM TB Simulated Wins
Diamondbacks 82.5 85 83 81 85 76.8 80 82.5
Braves 86.5 83 85 85 91 86.6 82 95.8
Orioles 78.5 75 81 75 82 79.2 76 86.2
Red Sox 82.5 85 79 85 84 80.5 83 97.2
Cubs 72.5 77 73 76 74 75.8 69 67.5
White Sox 80.5 76 83 76 80 85 78 64.2
Reds 90.5 92 84 86 90 91.1 84 92
Indians 78.5 80 74 79 80 76.8 85 87.9
Rockies 71.5 71 75 74 70 77.5 70 72.9
Tigers 92.5 91 86 95 91 89.7 95 94.5
Marlins 63.5 67 75 65 65 65.3 64 60.1
Astros 58.5 63 67 72 57 62.5 66 54.9
Royals 78.5 76 78 80 79 75 77 85.1
Angels 91.5 91 86 91 93 93.3 88 79
Dodgers 91.5 91 83 88 90 90.6 91 92.5
Brewers 81.5 78 83 78 81 77.6 78 73.3
Twins 68.5 65 74 69 66 70.9 66 69.6
Mets 75.5 80 78 76 66 76.8 74 73
Yankees 86.5 91 90 86 83 84.7 87 84.9
Athletics 84.5 83 86 84 78 85.3 85 94.6
Phillies 85.5 81 84 81 82 81 86 75.7
Pirates 77.5 80 77 81 77 74.8 79 92.1
Padres 73.5 76 78 76 73 72.7 81 76.1
Giants 87.5 85 85 92 87 85.1 88 75.2
Cardinals 82.5 85 86 83 85 85.1 90 94.6
Rays 86.5 87 88 86 88 89.5 93 89.2
Rangers 86.5 89 88 85 91 86.8 85 88.1
Blue Jays 88.5 84 78 86 94 87.5 82 73.8
Nationals 91.5 87 86 85 94 92.5 90 86.3
Mariners 77.5 78 79 73 74 74 78 71.4

Jim Caple and the pitcher win

ESPN’s Jim Caple just posted an article about the win statistic. This seems to be a response to Brian Kenny’s all-out assault on the win. Kenny is attacking the old stat as a grossly misleading, if not useless, statistic in measuring how good a pitcher is. I don’t really care if Kenny’s “kill the win” campaign gains steam or not; frankly, I don’t really care about wins (outside of my fantasy leagues where they count). I do think it’s outdated and doesn’t tell us much of anything. Matt Harvey has nine measly wins while having (pre-injury, obviously) a rookie season for the ages. Harvey still ranks second in pitcher WAR on Fangraphs (as of September 18- Kershaw, at least, will eclipse him before season’s end). Of course, it’s not Harvey’s fault he plays for the Mets (I suppose he could have refused to sign, like Elway and Baltimore or Cushman and Denver) but poor Harvey was enjoying the 13th lowest run support of all NL starters.

Caple admits to some shortcomings, but comes to the defense of the win:

Perhaps stat-heads would appreciate the win more if it was something else, though, something much more complicated and mathematically challenging. Maybe they would like it more if it included complex calculations that account for run support, adjusted ERA, advanced fielding analytics, WAR, stadium factors and humidity and was called tWIN.

I do love the idea that the win is simple and other sabermetric stats are, by definition, not. Caple talks about the “uncomplicated” win in an article that also discusses how on September 13, Cleveland starter Danny Salazar struck out 9 in 3.2 innings, but couldn’t get the win, because he didn’t go the required five innings. He talked about Drew Smyly vulturing Max Scherzer’s 20th win, after Smyly blew the lead. The rules on how wins are awards are full of inane loophools and requirements. Caple doesn’t even mention my favorite part of the win rule, which I’ll quote right out of the official MLB rules:

Rule 10.17(b) Comment: It is the intent of Rule 10.17(b) that a relief pitcher pitch at least one complete inning or pitch when a crucial out is made, within the context of the game (including the score), in order to be credited as the winning pitcher. If the first relief pitcher pitches effectively, the official scorer should not presumptively credit that pitcher with the win, because the rule requires that the win be credited to the pitcher who was the most effective, and a subsequent relief pitcher may have been most effective. The official scorer, in determining which relief pitcher was the most effective, should consider the number of runs, earned runs and base runners given up by each relief pitcher and the context of the game at the time of each relief pitcher’s appearance. If two or more relief pitchers were similarly effective, the official scorer should give the presumption to the earlier pitcher as the winning pitcher. [emphasis added]

The win isn’t a simple statistic just because it doesn’t have a mathematical formula. Like the RBI, there’s so many things that the player collecting it doesn’t control. That alone is why its value is so limited.

Caple boils down his argument to this with this:

Could the win be better? Sure. But one of the reasons I like the win is its simplicity. Despite its clear limitations, the win is a long-established and fun statistic that quickly tells us something about a pitcher — how many bad pitchers win 18 games in a season or 200 games in a career? — though by no means everything. Nobody is saying the win is the ultimate arbiter of anything for a pitcher. It’s just one of many stats for your consideration.

As I’ve already said, the win is not simple, and Caple makes the point himself. And he’s right, it’s one of many stats for you consideration, like paying $3 for a tin of Pringles on your next US Air flight is a food option; it’s not necessarily the best option for you to take.

As for players who accumulate big single season win totals or lots of wins over a career- does this mean much? In the days of complete games, when pitchers would rarely be lifted, the win meant something though still less than it seems lots of folks want it to. These days, with increased specialization and pitch counts and so on, it means even less. Christy Mathewson, to pick a random old-timey pitcher with longevity, averaged 8.67 innings per start for his ENTIRE 552-start, 17-year-long career. Mike Mussina is the closest modern starter in terms of games started to Mathewson, as Moose started 536 games. For his career, Mussina averaged 6.67 innings per start. Mathewson would need one out from a reliever, but he still needed the run support. Moose needed two innings and an out plus run support. To turn this a different way, Curt Schilling and Mike Mussina were both terrific pitchers in a big offensive era. Both were regular starters from 1992 until 2007. Schilling started 569 games to Moose’s 536, but Moose threw almost exactly 300 more innings (Schilling averaged 5.7 innings per start.) Schilling ended up with 54 fewer wins. Why? Mussina played for the generally good 90s Orioles while Schilling was pitching for the generally bad 90s Phillies. Similar service time, huge difference in win totals. Incidentally, Mussina is at 83.0 in rWAR and 82.3 in fWAR, while Schilling is 79.9 and 83.5. What seems like the better comparison? Wins, where Schilling is only 80% the pitcher Mussina was or WAR where they were equally good?

As for the specific win-totals Caple mentions, to counter that point I do need some fancy sabermetric, context-controlled stats, specifically FIP- and ERA- (each stat takes into account park factors and the league average numbers and compares them to other pitchers of that specific era;  100 is average, and the lower the number, the better. Someone who has an FIP- of 80 is 20% better than their peers) Lew Burdette was a solid pitcher from 1950 to 1967, mostly for the Braves. He collected 203 wins. He had a career ERA- of 101 and a career FIP- 103, both slightly below average. Denny McClain in 1966 won 20 games, and had an ERA 13% worse than the AL average and a FIP 23% worse. It’s actually not hard to find other examples of pitchers who were average or worse and managed to still win 18 or more games (here’s the custom search) but I suppose I can be discounted because I used ERA- and FIP-. Are any of these pitchers bad? No, none of them were Jose Lima for the Royals bad (incidentally, Lima had a 21 win season for the Astros in 1999. He was pretty good that year. I’m pretty sure there were a dozen or two pitchers in 1999 that weren’t named Jose Lima you’d have preferred on your team.)

I understand a desire for a simple number that can be understood quickly and tells us who was a good pitcher and who wasn’t. What escapes my understanding is this luddite position that Caple and others like him take, where they run towards this an archaic stat. I suppose there’s a bit of “it’s always been there.” But it strikes me a bit like someone in 1920 saying “we can’t get rid of the guys making horseshoes.” Horseshoes might be interesting, and occasionally might be useful, but they’re not longer a critical part of our world.

See more from this Tim guy at his blog or at Saturday Morning Deathgrip

On Exercise, BMI, and the fascination with strict recommendations

statsbylopez's avatarStatsbyLopez

Every Monday, Ph.D. students in the public health program at Brown gather to eat pizza, rearrange some unappetizing caesar salad around on our plates, and discuss a recent manuscript in different fields in an entertaining hour known as JournalClub. 

Today’s article of choice was written in American Journal of Health Promotion, linked here, which promoted the idea that short bouts of moderate to vigorous exercise each day were successful in reductions of BMI. The article was titled “Moderate to Vigorous Physical Activity and Weight Outcomes: Does Every Minute Count?” 

Methods, covariates, and study population limitations aside, what struck me as uncomfortable was how, despite the author’s self-admittance that this manuscript did not show causes and effects, the journal still placesets the following highlighted box. 

Image

 

In other words, “we can’t claim causation with our exercise exposure, but we urge you to change your lifestyle anyways.” Is that…

View original post 93 more words

NCAA Football – Top 25

Updated: September 15, 2013

 
 Rank Teams Record
1 OREGON 3-0
2 ALABAMA 2-0
3 WASHINGTON 2-0
4 AUBURN 3-0
5 UCLA 2-0
6 FLORIDA STATE 2-0
7 GEORGIA 1-1
8 GEORGIA TECH 2-0
9 CLEMSON 2-0
10 OREGON STATE 2-1
11 NAVY 2-0
12 INDIANA 2-1
13 USC 2-1
14 LOUISVILLE 3-0
15 OLE MISS 3-0
16 MIAMI-FLORIDA 2-0
17 ARIZONA 3-0
18 TENNESSEE 2-1
19 UTAH 2-1
20 NORTHWESTERN 3-0
21 ARIZONA STATE 2-0
22 SO CAROLINA 2-1
23 OHIO STATE 3-0
24 VIRGINIA 1-1
25 WASHINGTON ST 2-1

Full Rankings