One of the more unlikely scenarios three weeks ago comes to fruition, as the Cardinals and Tigers meet for all the marbles.
Not that this World Series is without its own compelling angles. For two teams that neither play in the same league nor share any obvious geographic connection, the Cardinals and Tigers have a fair amount of shared history. Detroit and St. Louis have tended to rise and fall together, both as cities and as baseball clubs, and this becomes one of a bare handful of World Series matchups that have occurred at least three times and haven't involved the Yankees:
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The NLCS becomes a battle just as the ALCS is edging towards an end.
\nMathematically, leverage is based on the win expectancy work done by Keith Woolner in BP 2005, and is defined as the change in the probability of winning the game from scoring (or allowing) one additional run in the current game situation divided by the change in probability from scoring\n(or allowing) one run at the start of the game.';
xxxpxxxxx1160835748_18 = 'Adjusted Pitcher Wins. Thorn and Palmers method for calculating a starters value in wins. Included for comparison with SNVA. APW values here calculated using runs instead of earned runs.';
xxxpxxxxx1160835748_19 = 'Support Neutral Lineup-adjusted Value Added (SNVA adjusted for the MLVr of batters faced) per game pitched.';
xxxpxxxxx1160835748_20 = 'The number of double play opportunities (defined as less than two outs with runner(s) on first, first and second, or first second and third).';
xxxpxxxxx1160835748_21 = 'The percentage of double play opportunities turned into actual double plays by a pitcher or hitter.';
xxxpxxxxx1160835748_22 = 'Winning percentage. For teams, Win% is determined by dividing wins by games played. For pitchers, Win% is determined by dividing wins by total decisions. ';
xxxpxxxxx1160835748_23 = 'Expected winning percentage for the pitcher, based on how often\na pitcher with the same innings pitched and runs allowed in each individual\ngame earned a win or loss historically in the modern era (1972-present).';
xxxpxxxxx1160835748_24 = 'Attrition Rate is the percent chance that a hitters plate appearances or a pitchers opposing batters faced will decrease by at least 50% relative to his Baseline playing time forecast. Although it is generally a good indicator of the risk of injury, Attrition Rate will also capture seasons in which his playing time decreases due to poor performance or managerial decisions. ';
xxxpxxxxx1160835748_25 = 'Batting average (hitters) or batting average allowed (pitchers).';
xxxpxxxxx1160835748_26 = 'Average number of pitches per start.';
xxxpxxxxx1160835748_27 = 'Average Pitcher Abuse Points per game started.';
xxxpxxxxx1160835748_28 = 'Singles or singles allowed.';
xxxpxxxxx1160835748_29 = 'Batting average; hits divided by at-bats.';
xxxpxxxxx1160835748_30 = 'Percentage of pitches thrown for balls.';
xxxpxxxxx1160835748_31 = 'The Baseline forecast, although it does not appear here, is a crucial intermediate step in creating a players forecast. The Baseline developed based on the players previous three seasons of performance. Both major league and (translated) minor league performances are considered.
The Yankees look for the Next Big Thing, the Padres wonder if they've already found it, and the Red Sox take the measure of Kevin Millar.
By most reports, the Next Big Thing actually turns out to be an old Big Thing: current Houston Astro and future Hall of Famer Roger Clemens. The theory is that Clemens, fed up with the fact that his 1.67 ERA hasn't earned him more than four wins, and that his ballclub is 21-34 and dead last in the NL Central, would demand a trade or else activate a super-secret handshake agreement that requires that the Astros trade the Rocket, not just to a contender, but to one which wears pinstripes and plays in the Bronx.
Interleague play has been cited as a key driver of attendance in its eight years. The data indicate that its effect is much smaller than MLB would have you believe.
There are two major divisions that can be made in terms of the schedule, both of which can have significant impacts on the number of people who will show up to a game. The first is the time of year, the summer months versus spring and fall months. Games played in June, July, and August are more likely to draw good crowds than games in April, May or September are. The weather is far more likely to be conducive to doing something outdoors during the summer. In addition, during the summer kids are out of school, and therefore families are more likely to come out to a game, especially a night game. The second division is weekday games versus weekend games, with weekend games obviously being more likely to draw a larger crowd. Since most Friday games are evening games and Sunday games are afternoon games, we'll call games on Fridays, Saturdays, and Sundays weekend games.
Putting these two factors together, you get that weekend games during the summer are likely to be the best for attendance while non-summer weekday games will be the worst. Let's look at the distribution of interleague and regular games using these distinctions for a typical team's schedule.
Last year at this time, when we were first unveiling PECOTA, I was besieged with questions about the system's accuracy. From the very start, the system has always had its believers and its skeptics; all of them wanted to know whether the damn thing worked. My evasive answers to these questions must surely have seemed like a transparent bit of spin doctoring. One of my readers suggested to me, quite seriously, that I had a future in PR or politics. But I was convinced--and remain convinced--that a forecasting system should not be judged by its results alone. The method, too, is important, and PECOTA's methodology is sound. It presents information in a way that other systems don't, explicitly providing an error range for each of its forecasts--which, importantly, can differ for different types of players (rookies, for example, have a larger forecast range than veterans). Its mechanism of using comparable players to generate its predictions is, I think, a highly intuitive way to go about forecasting. Besides, all of the BP guys seemed to appreciate the system, and getting the bunch of us to agree on much of anything is an accomplishment in and of itself. Now that it has a season under its belt, however, we can do the good and proper thing and compare PECOTA against its competition.
Last year at this time, when we were first unveiling PECOTA, I was besieged with questions about the system's accuracy. From the very start, the system has always had its believers and its skeptics; all of them wanted to know whether the damn thing worked.
My evasive answers to these questions must surely have seemed like a transparent bit of spin doctoring. One of my readers suggested to me, quite seriously, that I had a future in PR or politics. But I was convinced--and remain convinced--that a forecasting system should not be judged by its results alone. The method, too, is important, and PECOTA's methodology is sound. It presents information in a way that other systems don't, explicitly providing an error range for each of its forecasts--which, importantly, can differ for different types of players (rookies, for example, have a larger forecast range than veterans). Its mechanism of using comparable players to generate its predictions is, I think, a highly intuitive way to go about forecasting. Besides, all of the BP guys seemed to appreciate the system, and getting the bunch of us to agree on much of anything is an accomplishment in and of itself.
Hey, maybe they are America's team, because I ended up
getting a lot of thoughtful and articulate email from Braves
fans defending the acquisition of B.J. Surhoff.
Several of them tackled the subject of whether or not I gave
the Braves a fair shake as far as what B.J. Surhoff gives them,
especially in comparison to Al Martin.
The primary question raised by attentive readers is that when comparing
catchers against their counterparts in consecutive years, the counterparts
can change. That is, while Scott Hatteberg caught over 100 games for Boston
in both 1997 and 1998, the backup catchers changed. Bill Haselman got the
most of the rest of playing time behind the plate in 1997, while Jim
Leyritz, Jason Varitek, and Mandy Romero split time backing up
Hatteberg in 1998.
The Z-score method introduced in "
General Or Backstop?" rates catcher
performance in terms of standard deviations beyond the collective
performance of the other catchers on the team. If the collective
performance baseline changes from year to year, it could produce variation
in the catcher's Z-score even if his ability hasn't changed. That is, if
Hatteberg is actually an average defensive catcher in both 1997 and 1998
(in an absolute sense), but Haselman in fantastic defensively, Hatteberg
will be negatively rated in 1997 (since he underperformed Haselman). If
Varitek and company are poor defensively, the same performance by Hatteberg
would be rated highly in 1998 (since he outperformed the others). Given
this genuine concern, the question then becomes whether this effect is
substantial enough to skew the results and alter the conclusions of the
It wasn't that long ago, really. In 1992, 30 homers would have placed a
hitter fourth in the National League. These days, a player could hit 30
home runs and never show up on the typical fan's radar. We're in the middle
of the biggest home run jump in baseball history. (Big news, to you all,
I'm sure. Tomorrow's feature: the Pope wears a skullcap!)
If the sportswriters of the future aren't careful, then hitters of the '90s
are going to be seriously overrepresented in the Hall of Fame, the same way
that hitters of the '20s and '30s are today. People looked at the gaudy
batting averages of the era (Freddy Lindstrom hit .379 in 1930! Ooooooh!)
and instinctively viewed then through the prism of their own era (a .379
average in 1976, when the Vets' committee inducted Lindstrom, would have
been 40 points higher than any major leaguer actually hit that year).