What would the House of Commons look like under a Liberal-NDP merger? June 20, 2010
Posted by tomflesher in Academia, Canada.Tags: coalition government, divided left, Tories, Canada, Bloc Quebecois, Liberals, NDP, Canadian government, Canadian politics, hypothetical mango coalition, Whigs, no good reason
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It’s been a while since I did any Canadian politics ranting.
Coalition government and/or a left-wing merger in Canada is all the rage at the Globe and Mail, with a Globe and Mail editorial discussing the ramifications of a shift left, Jeffrey Simpson arguing that the whole thing is a stupid idea, and Neil Reynolds talking about the Whigs for no good reason. The arguments on all sides contemplate a merger or coalition of the Liberals and the New Democratic Party, which is the most logical assumption considering that the Greens are nonviable nationally (although I did enjoy discussing the “hypothetical Mango Coalition” that could result from the 2008 election if the red, orange, and green parties joined up).
I’m interested in the effect of a merger, so I’m going to make some assumptions, not all of which are warranted:
- The Bloc Québecois is not party to any coalition. BQ voters will always vote for the BQ. (This is probably the weakest assumption, since the BQ actively campaigns for votes and almost certainly won marginal candidates.)
- The Green Party is not party to any coalition. Green voters will always vote for the Greens. (Again, this is a fairly weak assumption and I might examine the hypothetical Mango Coalition in a later post, but they’re not considered relevant by the editorialists so I’ll ignore them. However, they would have made quite a difference in the model below.)
- Ridings won with a majority by any party remain with that party.
- A riding won with a plurality by a Liberal or NDP candidate would remain with the merged party regardless of the current vote split.
- A riding won with a plurality by a Conservative or BQ candidate needs to be reconsidered. I’ll do so by assuming that 66% of the NDP vote goes to the merged party and the other 34% evaporates (to model voters being displeased by a perceived shift to the middle and staying home). Based on those numbers, the party with a plurality takes the seat.
There were some surprising results. The Liberal-NDP merged party ended up poaching 24 seats in total, including 17 from the Conservatives and 7 from the Bloc Québecois. In total, that puts the parties at:
- Liberal Democrats: 137 seats
- Conservatives: 127 seats
- Bloc Québecois: 41 seats
- Independent: 3 seats
This puts a different spin on the current House. However, we must take into account the Bloc’s behavior. After the 2008 election, there was discussion of a Liberal-NDP-Bloc coalition government. However, it is not in the Bloc’s interest to form a coalition, since the Grits’ position on Québec sovereignty is not compatible with the Bloc’s. As a result, we must consider this a non-coalition government – a majority run by the Grits.
It’s difficult to imagine this situation as being much better for the Grits. Dion would have run a minority government, but as a weak leader he likely would have been forced into an election some time between October 2008 and now. Ignatieff would still have been waiting in the wings to take over the leadership of the party in the ensuing chaos.
A merger is not a panacea.
Modeling Run Production June 19, 2010
Posted by tomflesher in Baseball, Economics.Tags: economics, regression, sports economics, Baseball, run production
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A baseball team can be thought of as a factory which uses a single crew to operate two machines. The first machine produces runs while the team bats, and the second machine produces outs while the team is on fields. This is a somewhat abstract way to look at the process of winning games, because ordinarily machines have a fixed input and a fixed output. In a box factory, the input comprises man-hours and corrugated board, and the output is a finished box. Here, the input isn’t as well-defined.
Runs are a function of total bases, certainly, but total bases are functions of things like hits, home runs, and walks. Basically, runs are a function of getting on base and of advancing people who are already on base. Obviously, the best measure of getting on base is On-Base Percentage, and Slugging Average (expected number of bases per at-bat) is a good measure of advancement.
OBP wraps up a lot of things – walks, hits, and hit-by-pitch appearances – and SLG corrects for the greater effects of doubles, triples, and home runs. That doesn’t account for a few other things, though, like stolen bases, sacrifice flies, and sacrifice hits. It also doesn’t reflect batter ability directly, but that’s okay – the stats we have should represent batter ability since the defensive side is trying to prevent run production. The model might look something like this, then:
This is the simplest model we can start with – each factor contributes a discrete number of runs. If we need to (and we probably will), we can add terms to capture concavity of the marginal effect of different stats, or (more likely) an interaction term for SLG and, say, SB, so that a stolen base is worth more on a team where you’re more likely to be brought home by a batter because he’s more likely to give you extra bases. As it is, however, we can test this model with linear regression. The details of it are behind the cut. (more…)
Leadoff Home Runs June 19, 2010
Posted by tomflesher in Baseball.Tags: baseball-reference.com, Jose Reyes, leadoff home runs, Mets, Nate McLouth, Phil Hughes, Subway Series, Yankees
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Jose Reyes led off today’s Mets-Yankees game with a home run off Phil Hughes. That’s the eleventh leadoff home run of the year. That’s a little over half as many as there were last year on June 19, when Nate McLouth hit the 19th leadoff home run of 2009.
Last year, there were 51 leadoff home runs over roughly 6 months (early April through the first week of October), which puts uniformly distributed homers at 8.5 per month (so McLouth’s #19 on June 19 was about 2.25 behind pace). So far, with eleven over 2.5 months, that puts us on pace for 26.4, or, to be generous, about 30 leadoff home runs.
The change probably isn’t indicative of anything other than chance, but in 2008 #24 of 52 came on June 20, and in 2007 they were already up to 28 of 59 by June 19. Over the past few years there’s been a slowing of leadoff home runs which may be due to chance or may be due to some other factor. Who knows? It’s way too small a sample to say anything about.
Cell Phone Insurance June 18, 2010
Posted by tomflesher in Economics.Tags: cell phones, insurance, risk, t-mobile
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Yesterday, I bought a new phone. It’s a Samsung Gravity 2 and with a two-year contract it cost $79.99 – it came with some accessories that aren’t of interest for now. The salesman tried to sell me insurance at a whopping $4.99 per month over the course of the contract. I told him I’d do $4.99 total, because I’m an economist, but he didn’t bite. (Sigh.)
How bad a deal is that? Well, I wanted to find out. First, I made some assumptions:
- The appropriate interest rate is 1.25 APY (.1042 MPY), which is roughly what my bank account is paying. I could put some amount of money in the bank right now and earn interest at that rate and it would be enough for me to pay the insurance. This is called the Net Present Value, and over 24 months at 4.99 per month it’s about $118.34.
- The likelihood of something happening to my phone is entirely random, so I can’t take it into account when determining whether the insurance is a good buy.
- My phone depreciates at a rate of
, where t is the number of the month (so this month is month 1, next month is month 2, etc.). This puts my discount rate at exactly my APY. It makes for a quick depreciation, with the phone getting within a dollar of its resale value within about 4 months. It caputres the quick drop in depreciation an the slow leveling off quite nicely.
- The definition of ‘good value’ is that at the time I turn in a damaged phone, its depreciated value is less than the cost of all the premiums I’ve paid. I chose to use the depreciated value rather than the cost of a new phone because it reflects that I’ve gotten some use out of the phone.
The long and the short of it is that if I damage the phone before about the 7th month, it’s a good value. After that, it’s all gravy for T-Mobile.
I ended up telling the salesman that I’m an economist and so paying that much for insurance is against my religion.
For those who are interested in the chart, it’s behind the cut. It lists monthly payment, month ordinal, the effective interest rate, present value of that payment, NPV as sum of the present values, the depreciated value of the phone, the depreciation factor, and the instantaneous depreciation.
Appearances as Pitcher and DH June 17, 2010
Posted by tomflesher in Baseball.Tags: baseball-reference.com, Cardinals, designated hitter, Diamondbacks, Felipe Lopez, Jeff Kunkel, Mark Loretta, pitcher, Wade Boggs
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Earlier this year, Felipe Lopez pitched in relief for the St. Louis Cardinals in their 20-inning game against the Mets. Last year, he also played DH during an interleague game for Arizona. That made me curious how many players have at least one appearance each at DH and pitcher. I generated this table at Baseball Reference to check.
Several of these – for example, the bottom two in the list – were pitchers who started games at DH to allow their managers to insert hitting specialists when the DH came up. This led to a rule that the DH has to come to bat at least once unless the opposing team changes pitchers.
More interesting are the three at the top of the list – Jeff Kunkel, Wade Boggs, and Mark Loretta – all of whom have two seasons in which they both DHed and pitched. Loretta pitched an inning in 2001 and a single out in 2009, with Kunkel pitching for Texas in 1988 and 1989 and Boggs pitching for the Yankees in 1997 and the Rays in 1999. Hopefully the Cards will find an excuse to DH Lopez at some point this year just to even things out.
Quickie: Changing hosts June 17, 2010
Posted by tomflesher in Uncategorized.add a comment
After having a ton of trouble with my previous web host (and having a lot of downtime as a result), I switched over to WordPress.com’s domain mapping service. Hopefully this will be a lot more reliable.
June 15 Wins Above Expectation June 16, 2010
Posted by tomflesher in Baseball.Tags: Angels, Baseball, Rays, Tigers, wins above expectation
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Wins Above Expectation are a statistic determined using team wins and the Pythagorean expectation, which is in turn determined using runs scored by and against each team. The Pythagorean expectation is the proportion of runs scored squared to runs scored squared plus runs against squared. It’s interpreted as an expected winning percentage.
Wins Above Expectation (WAE) is then the difference between Wins and Expected Wins, which are simply the Pythagorean Expectation multiplied by Games played. It’s a useful measure because it can be interpreted as wins that are due to efficiency (in economic terms) or, more simply, play that’s some combination of smart, clutch, and non-wasteful. It rewards winning close games and penalizes teams that win lots of laughers but lose close games, since the big wins predict more games will be won when all those runs are spent winning only one game.
Using Baseball-Reference.com, I crunched the numbers for AL teams up to June 15. As usual, the Los Angeles Angels of Anaheim lead the league in WAE with 3.68, with Detroit’s 2.39 a close second, but the Tampa Bay Rays are a surprising last with -1.96 WAE. Obviously, this early in the season it’s too soon to conclude anything based on this, but the complete data is behind the cut. (more…)
Grand Slam, First Career At-Bat June 15, 2010
Posted by tomflesher in Baseball.Tags: batting order position, Daniel Nava, first career at-bat, grand slam, Jeremy Hermida, Kevin Kouzmanoff, probability, Red Sox
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On Saturday, Daniel Nava hit a grand slam in his first at-bat (hitting ninth for Boston). Needless to say, the odds against this are exceedingly long.
So far in 2010, there have been 1786 home runs hit in 73122 Major League Baseball plate appearances, for a rate of about .024 home runs per plate appearance. The American League has a league on-base percentage of .331 and the National League’s OBP is .329. That means that the prospect of any plate appearance ending in an out is (using .330 as the average OBP) .670. The likelihood of the bases being loaded at any point in an inning is the sum of three probabilities – three on base with 0, 1, or 2 outs.
![p[bases loaded, 0 out] = .33*.33*33 = .036](http://l.wordpress.com/latex.php?latex=p%5Bbases+loaded%2C+0+out%5D+%3D+.33%2A.33%2A33+%3D+.036&bg=ffffff&fg=000000&s=0 "p[bases loaded, 0 out] = .33*.33*33 = .036")
![p[bases loaded, 1 out] = .33*.33*.33*.67 = .024](http://l.wordpress.com/latex.php?latex=p%5Bbases+loaded%2C+1+out%5D+%3D+.33%2A.33%2A.33%2A.67+%3D+.024&bg=ffffff&fg=000000&s=0 "p[bases loaded, 1 out] = .33*.33*.33*.67 = .024")
![p[bases loaded, 2 out] = .33*.33*.33*.67*.67 = .016](http://l.wordpress.com/latex.php?latex=p%5Bbases+loaded%2C+2+out%5D+%3D+.33%2A.33%2A.33%2A.67%2A.67+%3D+.016&bg=ffffff&fg=000000&s=0 "p[bases loaded, 2 out] = .33*.33*.33*.67*.67 = .016")
![p[bases loaded] = .036 + .024 + .016 = .076](http://l.wordpress.com/latex.php?latex=p%5Bbases+loaded%5D+%3D+.036+%2B+.024+%2B+.016+%3D+.076&bg=ffffff&fg=000000&s=0 "p[bases loaded] = .036 + .024 + .016 = .076")
Note that this slightly overestimates the probability, since it ignores the likelihood of an extra-base hit. Obviously an extra-base hit would increase the chance that three people made it to base but one or more scored, leaving the bases unloaded.
Now, with a home run probability of .024, and a bases loaded probability of .076, the (again, slightly overestimated) probability of a grand slam is about .002, or .2%. That is, about one in every 500 at-bats should be a grand slam.
Since 1920, there have been only 10 people who have hit a home run and had 4 or more RBIs in their first game. The list is here. Of those games, six (including Nava’s) involved any player hitting a grand slam (including three hit by the rookie in his first game – Nava, Kevin Kouzmanoff on September 2, 2006, and Jeremy Hermida on August 31, 2005). Incredibly, both of them hit grand slams in their first career at-bats, with Kouzmanoff in the lineup as the DH in the #8 slot and Hermida pinch-hitting in the #9 spot.
Also interesting is that Hector Luna played with both Kouzmanoff and Hermida when they hit their grand slams, and that in 2009, the Red Sox had no home runs with runners in scoring position by the #9 hitter. Quite a turnaround.
(I should point out that Bill Duggleby also hit a grand slam in his first career at-bat in 1898, but that the searchable data doesn’t go back that far.)
Trends in DH use June 11, 2010
Posted by tomflesher in Baseball, Economics.Tags: baseball-reference.com, designated hitter, economics, Interleague play, Mets, regression, sports economics, Stuff Keith Hernandez Says, Baseball
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Last night, Keith Hernandez was talking about how the Mets are scheduled to play in American League parks starting, well, today. He pointed out that the Mets will be in a bit of a pickle because they aren’t built, as AL teams are, to carry one big hitter to be the full-time DH. Instead, an NL team will be forced to spread the wealth among lighter hitters who are carried for their defensive acumen as well as their offensive prowess. Keith then corrected himself and said that AL managers are using the DH differently – to rest individual players instead of having an everyday DH.
That pinged my “Stuff Keith Hernandez says” meter, and so I decided to crunch some numbers and see if that’s true. I interpreted Keith’s statement as implying that the number of designated hitters should be increasing, since managers are moving away from an everyday DH and toward spreading the DH assignments around a bit more. The crunching also needs to account for interleague play, which should obviously increase the number of DHes. So, after controlling for interleague play, does DH use show an increasing trend with time?
Early one-hitters June 11, 2010
Posted by tomflesher in Baseball.Tags: Baseball, baseball-reference.com, Jon Niese, Mat Latos, Mets, Padres
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Last night was an unusual confluence of events, in that the Mets lost the first game of a day-night doubleheader against the Padres and won the second game, with Jonathon Niese pitching a one-hit complete game in his 18th career appearance. That seems fairly unusual, so I generated a table with pitcher W, Complete game, 1 hit or less. It turns out that since 1920 there have only been 55 of them, and one of them belonged to the Padres’ game one starter, Mat Latos.
The complete table is here.
