The above line was a joke I threw at a couple people last night, because while the media wants to believe U.S. Hockey's 5-3 win over Team Canada last night was a 2nd Miracle on Ice, it wasn't.
- Olympic Hockey did not allow professional players to compete until the 1990's, and the team that U.S. Hockey fielded in 1980 was a group of amateurs, mostly college players.
- However, the Soviets, much like communist Cuba does today with their baseball team, did not formally allow players to emigrate to America to play pro sports. Thus the Soviet team was comprised of players good enough to play professionally and it showed: They had won 8 of the last 9 Olympic gold medals and hadn't lost a single game in 12 years to that point. Part of the reason the Soviets didn't allow emigration was to get a leg up in Olympic sports: They were, in terms of talent, head and shoulders above everyone else in the World.
Thus the Americans in that game were huge underdogs, akin in some way to, say, UNLV's basketball team playing the L.A. Lakers with both teams at full strength in a 48 minute game. For them to win that game in 1980 was indeed a miracle. It was a group of college kids basically beating a pro team.
That is not to say last night's U.S. win wasn't an upset and shouldn't be enjoyed. They were definitely underdogs in that game, and goalie Ryan Miller had to stop 42 shots, meaning his defense was getting outplayed and he had to rise above. But both teams in last night's match consisted of NHL players. Ryan Miller wasn't facing a squad that played at a level over his head. He was playing against a caliber of competition he sees several times a week, against star players he sees several times a year.
Last night's game was a fine upset, but it wasn't a Miracle on Ice. This, which happened 30 years ago today, was a Miracle on Ice:
Monday, February 22, 2010
Sunday, February 21, 2010
In my extended research on batter trends I've worked on trying to project stolen bases, which is a function of how many times you get on base as well as the frequency of your stolen bag attempts. A simple metric I developed for this purpose is steal rate, where you divide the total number of attempts (SB + CS) by the number of times a hitter gets on base ((PA x OBP)-HR). The 2009 AL average was .078.
Chone Figgins last year had a .205 steal rate. Good, right? However, I looked at past years and discovered a discouraging trend:
Steal rates by year:
The first thing that comes to mind is that, at age 31, Chone may be slowing down. However, last year with the Angels he also logged the most PA's he's ever had in a season, as he slid into a full time role at 3B. Could fatigue have also been a factor?
Steal rate by month for Chone Figgins in 2009:
Uh, yikes. Research turns up no data on anything in June 2009 that might have slowed him down. He didn't miss any significant time with injuries, and like all other 2009 starts he batted leadoff. And his career rates indicate that this isn't a typical pattern.
Career steal rates by month:
However, his career accuracy rates do tell:
Notice the sharp drop after a couple months, right down around and below the baseline success rate you need to make stealing worthwhile.
Whether it was a decision by Chone himself or the Angels management and coaches, could there have been an active awareness of Chone's declining success rate as the season progressed, and an according ease of steal attempts as the season wore on, especially considering that Chone is getting older and could lose a little bit of speed?
I can't confirm that. But if that was the case, it may not have worked: Chone only nailed about 71% of his steal attempts in 2009, so even as he dialed down those attempts, he was getting caught enough that the overall value of his base stealing was close to zero runs, possibly even below water. If we use Tom Tango's Markov derived run values (which TBH are a bit outdated), Chone roughly cost the Angels about half a run overall with his base stealing. That ignores the run expectancy context of his attempts, granted, but even adjusting for current data, it's likely Chone Figgins' baserunning is not at the level where he can make 60 stolen base attempts and help his team by doing so.
In a sense, batting Chone behind Ichiro may make sense as having Ichiro in front of him will a) block him from a few opportunities to steal and perhaps naturally reduce the number of times he attempts to steal and b) help provide a double distraction as a baserunner, create more pressure situations and perhaps improve the chances of some of his steal opportunities.
I don't expect Chone to steal 40 bases in Seattle. If nothing else, the average #2 AL hitter attempts 58% of the steals that the leadoff hitter attempts, and even given that the average 2-hitter isn't a base stealer, the fact that Chone will have someone in front of him roughly 35% of the time and will be on base with the Mariners' best hitters at bat far more often (which will dissuade steal attempts) will stunt Chone's opportunities to steal accordingly. So batting 2nd will take away some of the opportunities Chone would have had. In fact, it's likely he won't make more that 30-35 attempts this season. But the combination of circumstances along with a more selective approach should help improve his success rate, and 23-25 stolen bags certainly isn't out of the question.
Saturday, February 20, 2010
Over the last few seasons ESPN has promoted a Bracket Buster weekend slate of non-conference college basketball games, intended to help mid-major teams in undercovered conferences gain exposure in relatively high profile games. To ensure that at least a handful of worthwhile matchups result, ESPN scheduled 98 of these games between teams from a variety of conferences, including the Horizon (key teams include Butler and Cleveland State), MAC (Kent State), Missouri Valley (Creighton, Missouri State), Colonial (VCU, George Mason) and Ohio Valley (Murray State). Other smaller conferences are featured as well this time around.
To be honest, many of the participating teams have flopped this season and are playing out the string. Most of the better teams are far, far away from the NCAA Tournament bubble. Most have Sagarin ratings around the low 100's, which might not even be enough for an NIT bid. Some are legitimately in the hunt for at-large bids, though, like Butler and Utah State.
Mid majors in the hunt, however, even have a stake in games they're not involved in, because each win by one of their conference's teams can boost that conference's strength of schedule ratings, not to mention their own ratings, which in turn boosts the ratings of every team in that conference since these teams all play each other and thus factor into each respective strength of schedule. Obviously, if your conference gets hammered this weekend, that's not going to help your chances, while your conference doing well bolsters your chances even if you don't play this weekend, or your game was an easy win over a poor non-con foe.
Looking at current Sagarin ratings and utilizing probability methods, I determined the expected wins and expected losses for each conference based on the probability of each Bracket Buster game. For example, if a team has an 85% chance of winning their game, I gave that team and their respective conference 0.85 Expected Wins (E.W.) and 0.15 Expected Losses (E.L.). The underdog of course would get 0.15 E.W. and 0.85 E.L.
Here are the Expected Wins and Losses for each conference in this Saturday's games:
Wins and Losses by Conference
for Bracket Buster Saturday
The MAC, which is having a down year, is the most involved of the conferences, and while they're expected to pick up the most wins, they're also expected to pick up the most losses. The Ohio Valley has involved themselves quite a bit as well, but is looking to take a hit with more losses than wins. The top four participating conferences, however, are counting on a boost and expected to win more than they lose. In the case of limited participants, the Southern Conference has a fair chance at winning 2 of their 3 games, but the Big South is likely to take a beating and possibly lose 3 of 4.
It's likely that, outside of the Siena-Butler game, we don't flesh out any serious contenders from this bunch. The best of the lot have ratings in the 70's and are playing teams close in level to them. A win looks nice but proves little. However, it can help the conference a lot, plus could be the difference between a 14 seed and a 13 seed, which can always help your chances for an upset.
Friday, February 19, 2010
As Jeff Eisenberg noted, Minnesota recently paid tribute to teammate Paul Carter's cancer stricken sister by shaving their heads as a team before their 68-52 home upset against Wisconsin last night.
Eisenberg makes another key point: At 15-10 (with much of that record being buoyed by an easy non-con schedule) and being underwater at 6-7 in the Big 10, Minnesota's chances at the NCAA Tournament are dim. Stricken by arrests, academic issues and star recruit Royce White getting in trouble before bailing on the team for good... the Gophers under veteran coach Tubby Smith have had enough issues this year to make Tiger Woods' problems look mild in comparison.
Home losses to unhearalded Portland and a lowly Michigan team as well as a road loss to lowly Indiana and an 18 point pwnage by Ohio State is part of Minnesota's recycle-bin worthy 2009-2010 resume. Jeff Sagarin's ratings have the Gophers at 59th overall, with the bubble usually being around the low 40's... and that #59 ranking comes after the emphatic win over 13th rated Wisconsin.
All hope is not lost for a tournament bid, however. Minnesota can do more to pay tribute to Bria Carter than just shave their heads and steal a late season game from the Badgers. The good news is that despite their losses and middling rank in the Big 10, the win over Wisconsin wasn't so much of an inspired fluke: they can actually hang with the top teams in the conference. Sagarin's Predictor rating, different from their official rating in that it factors in margin of victory and is more accurate at gauging a team's chances vs other teams, rates them at 85.24, good for 25th overall in the nation. Compare that to top Big 10 teams Purdue (89.73) and the team they just beat at home, Wisconsin (89.14).
And speaking of Purdue, the Boilermakers are coming to Minnesota next Wednesday (2-24-10). Factoring in the home court bonus, Minnesota is literally a 50-50 shot against the current Big 10 leader. The rest of their schedule has nothing but winnable games, plus there's the Big 10 Tournament and its automatic NCAA Tournament bid for the winner. But it's possible for the Gophers to pad their strength of schedule enough to sneak into the tournament at-large. Their five remaining games: Indiana, Purdue, at Illinois, at Michigan and Iowa. The Selection Committee does show a bias towards late season charges by power conference teams, and Minnesota could net themselves a tourney spot given the following:
1. They need to beat Indiana, Michigan and Iowa.
These three teams, once-proud Big 10 powers, are now conference dogs, and Minnesota cannot afford a loss to any of them if they want to sneak in at-large. The sloppiness has to end now: Minnesota has to win these games. The home game with Indiana should be an easy win (92.3% chance of victory). Going to Ann Arbor will be much tougher since Michigan can to some extent play with the big boys (80.08 Predictor rating) but the Gophers are still a slight favorite (57.0%). The closer with Iowa is, like the Indiana game, an easy game (92.5%). The Michigan game will be a tough out, but they've got to win it, and of course there's no excuse for losing to Indiana and Iowa at this point.
Chances of beating all three: 48.7%
Chances of at least beating Indiana and Iowa: 85.4%
2. A win in Illinois with the three wins above would be nice, but if they lose to Michigan they must beat Illinois.
At #58 Sagarin, Illinois is roughly Minnesota's Big 10 equal, both in terms of rank and disappointment. Illinois is only a slight favorite at home (52.1%) so this is certainly a winnable game for Minnesota. It's not imperative that they beat Illinois, but at worst, between this and the Michigan game, they absolutely need to win at least one and at worst take a close, non-sloppy loss in the other. Winning both really helps, though doesn't necessarily make, their chances. Given a choice, Minnesota would much rather take a loss to Illinois and beat Michigan than the other way around.
Chances of beating all four: 23.3%
Chances of beating Indiana, Illinois and Iowa: 40.2%
3. Play Purdue close, at the least.
As mentioned, Minnesota has a real chance of beating Purdue at home (50.0%). But for selection's sake it is in their best interests to, at worst, keep this sort of close and, if they have to lose, to make sure it's in single digits. A close loss with 3-4 wins and Item #4 to come can be enough to indicate that Minnesota's worthy of an at-large spot. If they lose and the final margin's in double digits, however, that plus the Ohio State loss can well seal their at-large fate, even if they run the table in the other four games. This can't be a laid egg: Minnesota has to repeat their effort vs Wisconsin. Another upset win would be huge, and probably enough with 3-4 wins down the stretch to get them in on its own. But a close loss and a strong resume around it could be enough as well.
Chances of Minnesota running the table in their last 5 games: 7.6 to 1 (11.7%)
Chances of beating Indiana/Iowa/Illinois/Purdue: 20.1%
4. Get to the Big 10 Conference Tournament Semis.
This is going to be the biggest hurdle as, since Minnesota is likely going to be a #6 seed, the Gophers will need to pull at least one upset. Beating lowly Penn State in the opening round (80.9%) should be easy. It's beating Purdue again (35.8% on the neutral court), or avenging their ugly loss to Ohio State (39.4%), that's going to be tough. But they have to do it, or they're probably not getting in unless there are no upsets and a bunch of other bubble teams stumble themselves down the stretch.
Obviously, winning the tournament nets a guaranteed invite, but their chances of doing so are fairly slim. Assuming no upsets, their chances of doing that are somewhere around 18 to 1. Anything beyond making the semis (like, say, winning that semifinal game and hanging tough in the Big 10 Final) is in itself a bonus to their chances, but fairly remote.
Chances of making the semis: 30.4%
Chances of making the finals: 11.4% (8 to 1)
Chances of likely 7 seed Michigan helping Minnesota with a quarterfinal upset of Purdue or Ohio State: 20.3%
Minnesota's kind homage to Paul Carter's sister led to an inspired upset of highly rated Wisconsin, but Minnesota's moment in the sun doesn't need to end there. They've got a chance to undo much of the season's trashy mistakes and make more of this season than an NIT appearance without having to win the Big 10 Tournament. They can get into the tournament with at least 3-4 more regular season wins and a couple of Big 10 Tournament wins.
You can do it, Gophers. Get six more wins and get yourself into the Big Dance. If nothing else, do it for Bria Carter.
Tuesday, February 16, 2010
Cross-posted from my personal blog.
Earlier this evening my friend Eric Kaiser pointed out to me a revelation about the Winter Olympics, and a big factor that may indicate why I'm not into them and have never been into the Winter Olympics: These are rich people sports.
See, the Summer Olympics involve sports that don't require a ton of equipment. Running sports only require shoes (hell, if you're Zola Budd, you don't even need those). Swimming only requires trunks and a dedicated pool. Soccer and many other sports only require a ball. Few summer sports (like equestrian and shooting) require expensive, specialized equipment.
But in the Winter Olympics, every sport requires expensive equipment. Never mind all the jumpsuits, boots and cold weather gear: Skiing requires pricey ski equipment. Bobsleds require expensive, hard to find bobsleds. Luge and skeleton require a specialized sled and, for best results, a special aerodynamic jumpsuit. Snowboarding requires an expensive snowboard. Figure skating is an upscale event that calls for specialized figure skates and lavish uniforms. Hockey requires all sorts of padding, uniforms, hockey sticks, a special rubberized puck, skates (of course), and a dedicated ice rink. Many of these sports require the regular services of a personal coach to get any good at them (or at least get competent enough to avoid injury), with such coaches typically charging a pretty penny by the hour.
Plus, most of these winter sports can only be performed at remote mountain resorts that most people don't live near, so then participants must pay for travel and, when there for any extended period of time, overpriced lodging and food due to heavy demand in a remote region.
Most people in most nations (even the US) don't have the kind of disposable income to gain casual access to these sports, whether as a participant or a viewer. I sure don't.
Not only are the Winter Olympics limited to what cold-weather nations exist, but only those nations whose citizens have enough average disposable income to afford all the equipment that they can drum enough enough athletes to field competitive teams... which is why you see things like Canada Hockey demolishing the alleged 8th or 9th best team in the world by 10-15 goals, and why all the finalists are from the US, Canada and rich European countries.
In fact, the only two races I've ever noticed participating in the winter games are whites and Asians. Go figure those are the predominant races of the wealthiest countries with access to cold weather regions.
And don't flip the requisite crap about the Equator and how Latin Americans and Blacks don't do it, let alone can't generate enough athletes to contend for the podium, because they don't live near cold weather: South America's southern end is as far from the Equator as most of Canada. And as the subjects of Alive found out, the Andes Mountains get plenty of snow in the winter. The Andes cover seven countries: Argentina, Bolivia, Chile, Colombia, Ecuador, Peru, and Venezuela. That's quite a few Latin countries that could conceivably do winter sports. However, they pretty much don't, because all of the above countries are fairly poor (sorry, Hugo Chavez, that includes your hood) and most of the citizens can't afford to buy a few hundred bucks worth of ski equipment.
And last we checked, there are plenty of blacks in America, Canada and many European countries of varying descent that live near cold weather sporting regions. Hockey's had a handful of black players and there's been at least one black figure skater (Surya Bonaly of France). Plus, there is snow in Africa: They even have regular skiing in Lesotho. So blacks can and do participate in winter sports, and saying they have no access to it is culturally insensitive. Why don't we see blacks in the Winter Olympics these days? Never minding any social barriers the winter game cultures of participating countries may throw up, once again it comes back to money.
Hit it, Bryant Gumbel:
Count me among those who don't like 'em and won't watch 'em. In fact, I figure when Thomas Paine said “these are the times that try men's souls,” he must have been talking about the start of another Winter Olympics. Because they're so trying, maybe over the next three weeks we should all try too.
Like try not to be incredulous when someone attempts to link these games to those of the ancient Greeks who never heard of skating or skiing. So try not to laugh when someone says these are the world's greatest athletes, despite a paucity of blacks that makes the Winter Games look like a GOP convention.
Try not to point out that something's not really a sport if a pseudo-athlete waits in what's called a “kiss and cry” area while some panel of subjective judges decides who won. And try to blot out all logic when announcers and sports writers pretend to care about the luge, the skeleton, the biathlon and all those other events they don't understand and totally ignore for all but three weeks every four years.
He said that back in 2006, too. I'm not buying the hype: I still see it being true in 2010.
And you could say some of these economic factors are true of the Summer Games as well, though to be fair I've lost much of my interest in those as well over the last 15-20 years. But I've never had an interest in the Winter Games, and it has a lot to do with its socioeconomic relevance to my interests.
Monday, February 15, 2010
To me, Brad Bergesen's freak shoulder injury suffered while filming a 2010 Orioles commercial raises not the question of how he could let his conditioning lapse during offseason recovery from another injury, then try to throw at full speed just for effect in a 30 second spot ad.
No, my question is why Brad Bergesen was the subject of an ad in the first place.
Now, Bergesen is a one of Baltimore's many young, reasonably talented hurlers, and at age 24 with a useful 88-91 mph fastball and a decent slider, he's got potential. Though his 3.43 ERA in 2009 was a bit of the product of smoke and mirrors (4.42 FIP) and his minor league numbers in pitcher friendly East Coast leagues were solid but not necessarily inspiring... there's no reason for Orioles fan to be down on their young prospect.
But giving him his own ad? What, was Adam Jones busy? Are they not expecting as much from Chris Tillman? After Jones, Matt Wieters, Nick Markakis and Miguel Tejada, were they just fishing for one more guy of substance to round out the set of commercials, and he was just at the top of the list?
Because we're not exactly talking about Baltimore's answer to Tim Lincecum or Felix Hernandez here: Bergesen is a top pitching prospect, sure. But his numbers indicate more of a potentially reliable starter in the rotation, rather than Baltimore's next star hurler. If anything, Tillman and Brian Matusz are bigger possibilities to emerge as the next star pitcher for Baltimore's future.
If nothing else, it indicates how thin the star power pool is in Baltimore, which rivals DC's Nationals not just as a regional sister team, but in their competitive irrelevance and lack of household names.
Using Baseball Prospectus' Run Expectancy data from 2005-2009, I devised a weighted composite run expectancy for use in further analysis of marginal run expectancy changes from events.
|Composite Run Expectancy By Out and Baserunners|
|Situation||0 outs||1 out||2 outs|
|Man on 1st||0.896||0.535||0.227|
|Man on 2nd||1.149||0.697||0.333|
|Man on 3rd||1.395||0.969||0.365|
|1st and 2nd||1.505||0.922||0.458|
|1st and 3rd||1.776||1.188||0.505|
|2nd and 3rd||2.020||1.422||0.581|
Assuming equal weight to each season's run expectancy totals, the above chart utilizes a somewhat arbitrary 50-30-10-6-4- scale, weighing the 2009 season most heavily (50%) down to the least-weighted 2005 season (4%) to give better credence to more recent MLB run environments.
This will be the matrix I use in any further research that requires a run expectancy matrix.
Eyeballing the variance between seasons, the data is roughly consistent across the last five years. There didn't appear to be a dramatic trend beyond a subtle, general decrease in expected runs for several events over the last five years. But I preferred to emphasize recent trends over emphasizing the distant past and recent past equally given the always-changing MLB environment.
Sunday, February 14, 2010
While continuing work on my projection methods, I stumbled upon Baseball Reference's stats by ball and strike count. Linked are 2009's breakdown for the AL and NL.
I suddenly had an idea to try and estimate the run values of every pitch. Sabes are exploring at length the idea of using Pitch F/X to extrapolate the value of curveballs, fastballs, etc. But I've elected to take a simpler approach: Who cares what pitch is used? What should ultimately matter is that the pitch thrown in a particular count results in a particular different count or an outcome. Whether that new count resulted from the quality of the pitch or if it resulted from fooling or not fooling the batter can be subject to all the debate you wish. But ultimately, the pitch worked or it didn't. The pitcher got farther ahead or the hitter, he fell farther behind, the pitcher got beat or he beat the hitter.
I decided to try and estimate the run expectancy of a given count. One way to do this is to explore every known instance of each count using game logs, extrapolate the outcome and crunch an average run expectancy, but since I'd rather not spend the rest of my life locked in a room crunching game logs, I'd rather estimate. And methods exist to approximate the run value of different situations given historical outcomes.
As Dan Fox outlined in 2006, Pete Palmer and Dick Cramer created the Batter's Run Average stat (BRA) in 1974, the precursor to what eventually became OPS. By multiplying a batter's OBP and SLG together, they could get a rough estimate of how many runs the batter averaged per plate appearance. For example, a player averaging 250/300/400 averages 0.12 runs per plate appearance. Over 600 plate appearances in a season, that player would expect to produce 72 runs as a hitter.
Of course, as Fox notes in his piece, BRA ignores defense and lacks some of the advantages of OPS, like its relative ease to calculate and park adjust. I wouldn't necessarily use BRA to blindly estimate a player's production, unless I park adjusted his raw stats first, used approximated stats from batted ball rates, and I lacked better methods otherwise to project said player.
However, BRA can have value in estimating the run value of ball and strike counts for an entire league. Since I'd use the raw stats for the entire league, there's no need to park adjust the raw numbers (though you might want to park adjust the final numbers). And by using raw stats over multiple years, I should have a large sample that should all but erase the effects of variance.
So by taking the OBP and SLG for each individual count, I took the 2009 stats for each league and approximated the expected run value for an average hitter in each given count. I took not the stats for a ball put into play on a given count, but the stats for an hitter that ended up in a given count regardless of when the eventual outcome occurred. Below are the BRA's for each count by league in 2009:
2009 AL run expectancy by pitch per BRA
Entering PA - 0.144
1-0 - .184
2-0 - .265
3-0 - .384
0-1 - .101
1-1 - .128
2-1 - .174
3-1 - .296
0-2 - .054
1-2 - .068
2-2 - .096
3-2 - .178
2009 NL run expectancy by pitch per BRA
Entering PA, position players - .142
As pitcher - .032
1-0 - .176
2-0 - .258
3-0 - .365
0-1 - .094
1-1 - .119
2-1 - .172
3-1 - .292
0-2 - .051
1-2 - .065
2-2 - .095
3-2 - .181
Notice that for the NL I separated position players and pitchers, since pitchers in general are very poor hitters and their low hitting numbers skew the NL hitters' data. However, the pitchers' data is included in the ball/strike data since I could not parse pitchers from that data, and as a result the NL's BRA by count does trend slightly lower in most counts, but not by a large amount.
Never the less, from all this you could follow each pitch for a pitcher and, after each pitch, estimate a marginal run value.
What happens once the ball's put in play or the plate appearance otherwise concludes? Using BRA and the outcomes of groundballs, flyballs and line drives over the last five seasons (2005-2009), I also estimated the run values via BRA of balls put into play by type. However, Baseball Reference's flyball data includes all flyballs, whether pop flies, home runs or flyballs into the outfield, even though all three have very different contexts. A pop fly would stand to have nearly no run value since they are caught for outs 98-99% of the time.
A home run would of course be an automatic run or runs: However, BRA's estimated value of 4.0 runs (the 1.000 OBP times the 4.000 SLG for a home run) isn't accurate since it's not going to score 4 runs every time you hit one unless in the unlikely event you hit nothing but grand slams. Likewise, a walk or HBP's estimated BRA of 0.0 (1.000 OBP x .000 SLG) isn't accurate since many free passes will eventually result in runs.
Using old data (1974-1990), Tom Tango estimated the average run value of a home run at 1.402 and though more current data has likely updated that number... a simple, obvious and more accurate way to contextually assess the value of a given home run would be to simply count the number of runs each individual homer scores. If a home run's hit with two men on, simply count it as 3.0 runs, since after all that's how many it's scored. Run values for all other events are estimated because we aren't certain at the time of the outcome, but a home run's outcome is absolutely certain when it happens.
Run values for walks, hit by pitches, strikeouts, balks and other events that move runners can be devised using a reliable run expectancy chart or matrix (Tom Tango has a nice example of one using data from 1999-2002). You simply take the run expectancy going into a situation, find the run expectancy of the new situation and find the marginal difference.
With all this in mind, here is how you can devise the approximate run values for given outcomes.
Line drive - .735
groundball - .063
in-play flyball - .053
pop fly - .000(5)
home run - Number of runs actually scored
Walk/HBP/other outcomes - The net difference in run expectancy per run expectancy matrix
Using all this, you could graph over the course of a game the run expectancy of a pitcher's performance, and over time gauge the net run value added or prevented over a game and even a season. You can also do this for hitters. Obviously the ball/strike count expectancies wouldn't matter in a player's net run value produced once a plate appearance concludes, but it can help if you were going to graph a player's plate appearances in logging how each pitch improved or decreased the player's run expectancy.
And obviously, this would mean tracking and perusing the game logs and maybe even... heaven forbid... watching the games, instead of just glancing at the cumulative stats and crunching estimates, to get all this data. But as baseball research develops, we ought to get used to taking closer looks at the game, rather than finding excuses to make guesstimates by tracking it from a distance.
Apologies for the extended absence. A new work assignment and a work side project intervened and pretty much ate all of my time this last week.
Spring training rapidly approaches at MLB, and it's time to start taking a close look at 2010's talent and figure out how key players are going to contribute.
One key to evaluating how players will perform is to gauge where in the lineup they hit. Most will argue that it doesn't matter where in a lineup a player hits, and over an individual game that may be the case. But over a season, batting a better player higher in the order will give that player more opportunities to contribute offensively to your team. Average plate appearances for each batting spot by league in 2009:
1st - 763
2nd - 746
3rd - 728
4th - 711
5th - 696
6th - 681
7th - 662
8th - 643
9th - 622
1st - 762
2nd - 743
3rd - 725
4th - 709
5th - 693
6th - 675
7th - 657
8th - 638
9th - 618
There is a reason Ichiro insists on batting 1st in the Seattle Mariners lineup, instead of 2nd or 3rd. Batting 2nd costs him about 20 PAs a year, 20 opportunities to get hits, over batting leadoff. Batting 3rd costs him about 40 PAs. Since he puts the ball in play so frequently and hits for an average in the .300-.350 range, that's a difference of as many as 10-15 hits, a big deal for him given he takes great pride in his growing hit total.
Though many sabermetric analysts will claim there's no huge difference in where you bat a good hitter, there is indeed a tangible benefit to batting your best hitter leadoff.
But back to the point: For fantasy analysts as well as sabermetric analysts, gauging a player's potential output also hinges on where in the lineup he'll hit, not necessarily for guesstimating RBI totals, but to gauge how many PAs and ABs he'll get in a season. A player batting leadoff will see 150 more PAs over a full season than a #9 hitter, and 60-65 more than a #5 hitter. For a hitter with, say, a .350 OBP and .450 SLG, every spot up or down the lineup can make a difference of about 3 offensive runs over the course of a full season.
In a head to head fantasy league, it may not make a difference except in very close matchups or in siphoning a small degree of scoring potential from each and every matchup. But in a Roto league this difference is huge, as the runs/hits/etc you could get from an extra 60-150 PAs in a player could be a huge difference maker. And for baseball clubs itself, it can make a big deal both ways: A good hitter shuffled down the lineup can cost you as many runs as batting a poor, overrated hitter higher in the lineup. Lineup position matters.
Sunday, February 7, 2010
Super Bowl XLIV (Miami, FL)
Indianapolis Colts vs New Orleans Saints
Favorite: Saints (52.5%)
This was one of the best Super Bowl matchups we could get given the teams, not as much because each team was the best of their respective conferences, but because both teams have high-powered passing games and suspect pass defenses, which plays to both teams' offensive strengths. To call the Saints the 'favorite' is a bit of the misnomer: It's more like they have a 53-47 edge in what's actually a close matchup.
Colts Offense (with grade):
Points Per Drive: 2.43 (B+)
Drive Success Rate: .748 (A+)
Turnovers per: .140 (C)
The Colts' biggest issue, aside from their token running game, is that they are somewhat prone to turnovers and face a Saints defense that generates a strong number of turnovers. Together with somewhat slippery game balls and one of Brett Favre's dumbest throws ever, they helped generate several turnovers in their NFC title win over the Vikings.
Offense Line Run Blocking: D+
Left End: F
Right End: C
Pass Protection: A+
QB: Peyton Manning: A+
RB: Joseph Addai: B- (Receiving: B)
RB: Mike Hart: D
RB: Donald Brown: D (Receiving: A)
WR: Reggie Wayne: B
WR: Austin Collie: B
WR: Pierre Garcon: C
WR: Hank Baskett: F
TE: Dallas Clark: A
TE: Tom Santi: B
TE: Jacob Tamme: F
It's a good thing Peyton Manning is arguably one of the greatest QBs ever to play in the NFL and that he has good receivers, because his running game does not play a huge factor in the Colts offense. Whether it's because of spotty run blocking or lacking abilities on the part of non-Joseph Addai tailbacks, the Colts can't count on much from the backfield on the ground.
As for the passing game, the Saints can match up well with Reggie Wayne, and might be able to somewhat contain Dallas Clark, but they are average overwise, which should open doors for Austin Collie.
One other key factor: The game is outdoors, and both teams are dome teams. Manning over his career has been a good QB whether at home or on the road, though Manning does have more picks on the road (101) than at home (80). However, this year he posted better numbers on the road (112.6 rating) than at home (89.9). And one more factor in Manning's favor: He has 4 career regular season games vs the Saints, and has done well against them: 75 of 115, 1173 yards, 11 TD, 4 INT, 116.3 rating. They can pick him (4 picks in 4 games), but he can shred them like he can anyone else.
Once the wind and elements comes into play, however, it's always going to be more difficult to throw the football outdoors than inside, no matter what. This never minds the condition of a grass field, though by all accounts the turf at Miami's Landshark Stadium was consistently firm and held up well during last week's Pro Bowl.
Colts defense (Base 4-3):
Overall: C (Momentum Weighted*: C+)
Points per Drive: 1.64 (C)
Drive Success Rate: .688 (C-)
Turnovers per Drive: .144 (C)
* - Weighed to emphasize late season performances over early season performances
Run Defense: C
vs left end sweeps: C
Right DE: D-
Interior run defense: C
Left DE: D
vs right end sweeps: F
Pass Defense: C
Defensive line vs rush: D-
Pass rush: C
vs #1 WR: C
vs #2 WR: D+
vs Other WR: C
vs TE: C
vs RB: C-
Never mind the injury to Dwight Freeney: The Colts defensive unit in itself is fairly overrated. Freeney and Robert Mathis are the only threats in an average pass rush. As run defenders they get run over. The run defense, beyond the raw stats, is actually fairly average per play, and weak when opponents run off-tackle. The pass defense is average at best, and frequently beatable. Expect big games from Drew Brees, Robert Meachem, Pierre Thomas and of course the emerging Reggie Bush.
Colts Special Teams:
Kicker: C (Kickoffs: C)
Kick returns: C
Punt returns: D+
Points Per Drive: 2.56 (A)
Drive Success Rate: .742 (A-)
Turnovers per: .148 (C)
Like the Colts, the Saints offense has a high powered offense that doesn't protect the ball as well as it shouldn. Unlike the Saints, however, the Colts aren't especially hawkish for the ball, and chances of the Colts generating turnovers aren't as likely.
Offense Line Run Blocking: A
Left End: C-
Right End: A-
Pass Protection: B+
QB: Drew Brees: A+ (Rushing: A+)
RB: Pierre Thomas: A (Receiving: A-)
RB: Mike Bell: C
RB: Reggie Bush: A (Receiving: C)
WR: Marques Colston: A
WR: Robert Meachem: A+ (Rushing: A+)
WR: Devery Henderson: B
WR: Lance Moore: A
TE: Jeremy Shockey: A
TE: David Thomas: B-
Also unlike the Colts, the Saints have a good running game, and Freeney or not, expect the Colts to get run over frequently. Of course, the Saints true offensive strength is their passing game, and the Colts defense gives little indication that they'll be able to slow down, let alone stop the Saints' impressive passing game.
Saints Defense (Base 4-3):
Overall: C (Momentum Weighted*: D+)
Points per drive: 1.71 (C)
Drive success rate: .670 (C)
Turnovers per drive: .187 (A-)
* - Weighed to emphasize late season performances over early season performances
Run Defense: D-
vs left end sweeps: D-
Right DE: F
Interior run defense: C
Left DE: C
vs right end sweeps: A+
Pass Defense: C+
Defensive line vs rush: C-
Pass rush: C
vs #1 WR: A
vs #2 WR: C-
vs Other WR: C
vs TE: B
vs RB: C
Beyond their ballhawking tendencies, the Saints defense isn't particularly strong. They defend the #1 receiver and tight end well but are just average in covering anyone else. Their run defense isn't too good, for some reason prone to getting blown up on left-side outside sweeps while impressive in shutting down sweeps to the (right handed) QB's throwing side. Their left side does an alright job against the run but the right side gets owned too often.
Fortunately for the Saints, the Colts aren't a big running team and probably won't take advantage of the holes up front too often. But Peyton Manning is going to pick apart the Saints secondary, so long as he continues his excellent job of reading the coverage and doesn't walk into a blindside pick or two.
Saints Special Teams:
Kicking: D- (Kickoffs: C)
Kick returns: B
Punt returns: D
Whatever edge a good kick return game would get the Saints is blown by a poor kicking game that's been disguised all season by the Saints' prolific offense and winning ways. This will become a much more pronounced weakness in the outdoor conditions, especially if the game is at all close and field position plus field goals become a key issue.
So do the Saints really have the edge?
Only in the slightest sense of having a few more advantages than the Colts have. But otherwise, both teams are similarly strong and possess similar weaknesses. This is going to be a high scoring game, and the only way you could convince me to put money on this game is to take the over on the over/under line regardless of the number. But otherwise, I'd feel it foolish to put money on either team, even with the Saints getting +4 on the point spread from Vegas as the nominal underdog. Any dramatic swings during the game could turn the tide completely.
It should be a fun Super Bowl, so enjoy!