Tag Archives: baseball

BASEBALL PARTY FUN TIME 2K18

Things I should be working on:

  • NaNoWriMo
  • Class notes
  • Class review questions
  • Answering emails

Things I worked on instead:

  • This freaking blog

So remember that baseball thing I’ve done a couple times now, once for 2016 and once for 2017? I did it again for 2018 because why the hell not.

Let’s do the CopyPaste Dance from a previous blog:

At the end of the regular baseball season, you can see how many wins each team got out of the total number of games they played, and then rank the teams by their performance (who had the most wins, the second most wins, etc.).

What I want to do is see how this “real” data correlates with how many wins each team would get if they scored their average number of runs per game in every single game they played. For example, if the Athletics score an average of 5.02 runs per game, how many of their games would they have won by scoring 5.02 runs in each of those games?

(Yes, I know you can’t score 0.02 runs in a single game, but just work with me here.)

The process:

  1. Record each team’s average runs per game (I’ll call this “RPG”) (from here).
  2. Sort teams from highest to lowest RPG.
  3. Now, if a team A has a higher RPG than team B, that would mean that A would win every game they play against B. So the next step was to figure this out for each pairing of Team A versus Team B.
  4. I used this logic for all pairings (numbers of games per pair was obtained from here), then summed across the rows to get the “predicted” number of wins based on RPG alone. Then I compared looked at how each team’s “predicted” number of wins compared with their “actual” number of wins, and ranked each team by both their “predicted” and “actual” values.

How do they compare for the 2018 season?

11-02-2018

Boston (highest RPG) would win every game they played; Miami (lowest RPG) would lose every game they played. Bummer.

Correlation of RPG-predicted games won and actual games won: 0.797

Correlation of team rankings based on RPG-predicted games won and actual games won: 0.832

Pretty cool.

The biggest discrepancies, of course, are at the extremes. Based on RPG alone, Boston was predicted to win 66 more games than they did; Miami predicted to lose 66 more than they did. The smallest discrepancy is for the Angels, who were predicted to win two more games than they did.

FUN!

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METS, NO

Mets, why you gotta go trading my favorite dude? Not cool.

I was legit sad when I read that Cabrera got traded. I dig Cabrera. Now he’s with the Phillies, the team against which he had the famous bat flip.


Edit: Did you know Cabrera has one of the 15 unassisted triple plays in MLB history? Now you do!

I’m sad.

HEY NOW, YOU’RE AN ALL-STAR

Dudes, the All-Star game was awesome. I actually don’t remember if we watched last year’s, but this year’s is definitely more memorable if we did.

It’s pretty cool to see all the big stars of baseball come together and play a game that’s not taken too seriously. Especially since we mostly watch NL East teams (due to Mets); it’s cool to see the other top dudes not in the NL East or even in the National League in general, like Trout and Judge and Altuve.

I also like how they mic up some of the dudes and interview them while they’re out on the field.

Yay baseball!

“Up Close on Baseball’s Borders”

Okay, so I know this is a fairly old article now and things have (maybe?) changed, but I’m cleaning up my bookmarks and found this again, so here it is for y’all’s viewing pleasure.

Poor Mets, man. The fact that the Yankees had to be completely removed from the options to make that “New York Mets vs. Philadelphia Phillies” map possible is pretty bad.

Here’s the full interactive map.

It would be interesting to see Canada, too. I suspect Blue Jays everywhere, even in places like Vancouver, despite its proximity to the Mariners.

(I apologize if I’ve blogged about this before. I don’t think I have…)

BASEBALL BASEBALL BASEBALL

Holy freaking crap apples, I’d forgotten how much I missed watching baseball. The season started yesterday and so Nate and I watched the Mets game last night (they won, yay!) and are spending today watching a few of the other games from yesterday/today.

(Because Good Friday is a “you get the day off from work” holiday up here, which is weird to me.)

So despite the fact that it’s super cold and windy outside right now and it feels more like early February than ALMOST FREAKING APRIL, spring has now officially begun because baseball has started up again.

Yay.

QUANTUM FARTS

So Nate showed me this article a while ago and I keep forgetting to blog it—which is strange, ‘cause the article is hilarious—so here we go.

The Perfect Baseball Game is 19 Hours Long and has Lots of Crying

 

 

Canadian Baseball League Idea!

Nate bought me Out of the Park Baseball 18 because he knows I’m super into baseball now and this game allows you to do basically every possible baseball simulation you can imagine.

We are currently in the doldrums between baseball seasons, but I think what I want to do once the regular season starts up is to make a fake Canadian baseball league (the Canadian Baseball League, maybe? CBL?) with teams in some of the major Canadian cities. I can make the teams and then simulate the year with those teams and see how it goes. I think that would be super cool!

(Edit: yeah, I got way too busy to do this. But I still want to make it happen! Maybe next season).

WORLD SERIES

YAY ASTROS!

That was a pretty intense World Series. I’m glad the Astros won, mainly because they hadn’t won before.

(And because of Altuve. Short people represent!)

It wasn’t quite as intense as the World Series last year, but it was still pretty awesome. Now, unfortunately, no more baseball until spring.

Hopefully the Mets will have a better season next year…

Heights

Heyooooo so I was looking for some new data to update one of my STAT 213 note sets and I decided to use the on-record heights for 500 baseball players. We’re learning about types of graphs (boxplots, histograms, etc.) and ways of describing the shape of graphs (symmetric, skewed, etc.) so I thought hey, let’s plot these heights and see what we get.

10-08-2017-a

UHHHH, have you ever seen such a perfect bell-curve shape for actual data?

But here’s where it’s interesting to also look at not only the numeric summary but other plots as well. The boxplot actually shows two outliers (two players at 80 inches tall).

10-08-2017-b

So that’s interesting as well.

Anyway. Just thought this was a super pretty distribution and I have no life so I wanted to share this with y’all.

Oh, Mets…

Hahaha, wow, when it’s all put together in a little montage like that…

That 2015 World Series, holy hell.

Baseball Stat Party Fun Time

A fun project!

At the end of the regular baseball season, you can see how many wins each team got out of the total number of games they played, and then rank the teams by their performance (who had the most wins, the second most wins, etc.).

What I want to do is see how this “real” data correlates with how many wins each team would get if they scored their average number of runs per game in every single game they played. For example, if the Mariners score an average of 4.74 runs per game, how many of their games would they have won by scoring 4.74 runs in each of those games?

The process:

  • Record each team’s average runs per game (I’ll call this “RPG”) (from here)
  • Sort teams from highest to lowest RPG

Now, if a team A has a higher RPG than team B, that would mean that A would win every game they play against B. So the next step was to make a grid like this and fill in the number of times each pair of teams played each other.

08-22-2017-a

Boston has a higher RPG than the Rockies (5.42 and 5.22, respectively). So that means Boston would score 5.42 runs and the Mariners would score 5.22 runs in every game they played against each other. So of the 7 games played where these two teams faced each other, that would mean that Boston would win all of them.

I used this logic for all pairings (numbers of games per pair was obtained from here), then summed across the rows to get the “predicted” number of wins based on RPG alone.

How do they compare for the 2016 season?

08-22-2017-b

Boston (highest RPG) would win every game they played; The Phillies (lowest RPG) and the Athletics (bad luck) would lose every game they played. Bummer.

Correlation of RPG-predicted games won and actual games won: 0.640

Correlation of team rankings based on RPG-predicted games won and actual games won: 0.683

Interesting!

MLB: Do Run Totals Matter?

So after watching the Mets get eaten by the Nationals on Sunday, I wanted to see if the total number of runs a team scored throughout the course of the season had any significant correlation with the number of wins they had in that season.

(This something that has probably been done to death by actual baseball stats people, but give me a break, I’m new to this and I feel like playing around in Minitab).

First, just some general scatterplots of runs vs. wins.

By league.

And by division.

The overall correlation between runs and wins, for all teams combined, is 0.541
(p-value = 0.002).

Now let’s break it down!

By league

  • AL: 0.646 (p-value = 0.009)
  • NL: 0.444 (p-value = 0.097)

By division

  • ALC: r = 0.433 (p-value = 0.467)
  • ALE: r = 0.746 (p-value = 0.148)
  • ALW: r = 0.883 (p-value = 0.047)
  • NLC: r = 0.877 (p-value = 0.051)
  • NLE: r = 0.869 (p-value = 0.056)
  • NLW: r = -0.119 (p-value = 0.849)

Interesting. The only significant correlations are for all the teams combined, the AL, and the ALW (and even that one’s borderline).

Anyway.

IT’S BASEBALL O’CLOCK

It’s Mets time, bitches! Hopefully they’ll win their first game. And, y’know, a good amount of the rest of their games.

(Edit: haha, wow, they murdered the Braves.)

Also, shock of shocks: I dug out my old Unreal CD and stuck it in Big Compy just to see how badly it wouldn’t work. But hey…it actually worked! And it looks shockingly good for a game from 1998.

Awesome. I just wish The Neverhood worked on this computer, too.

Reekris

Yay, USA won the World Baseball Classic!

I’m ready for the regular season to start. Nate got me all excited about baseball last year, haha.

Edit: unrelated, but important.

THE BIG YELLOW ONE IS THE SUN

Holy crap, that was the most insane baseball game! Even if you’re not a huge baseball fan (or a baseball fan at all), hopefully you got to watch at least the last game of the World Series.

Edit: If you missed it…

Ridiculous. Awesome. I’m so glad Nate and I turned on the game after Cleveland tied it (we were going to wait and watch it later, because that’s what we were doing with all the playoff/World Series games).

Yay Cubs!

The Mets, The Mets, The Mets are On Fire

Take a look at what the Mets are doing right now:

09-07-2016-a

This is a graph of the Mets’ probability of making it to the postseason. As you can see, the probability had been taking a dive for most of the season, bottoming out at 6.7% on August 19th. They basically had a very, very small chance of making it.

Less than a month later, that probability has shot up to 63%. That’s pretty crazy. They’re just out of the wildcard spot now. I think it’s especially interesting when you consider that none of the other teams even remotely in contention have any huge upswings or downswings (except maybe the Cardinals).

09-07-2016-b

(Sorry, I like graphs.)

(And now I can use the “sports” category for the first time in like 4 years.)

Road Trip – Day 13: San Francisco (Giants/Braves Baseball)

Today Nate and I braved the streets of San Francisco to walk along the waterfront (with the secret motive to obtain candy) and then to get to the Giants/Braves game at AT&T Park.

The waterfront was a bit less terrifying than the non-tourist part of San Fran, at least. And we found IT’SUGAR, which is pretty much the best candy store ever (though it’s more expensive than our candy store in Calgary). We each got 2+ pounds of candy, ‘cause we’re addicts awesome.

The game was super cool, too. Baseball is so weird without the TV announcers! The Braves lost, which is too bad, but it was fun. Pictures!

39

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Tomorrow we’re getting the hell out of San Francisco as fast as we possibly can.