What National Polls Tell Us

If one spends time on American political Twitter, particularly whenever a national Presidential poll is the subject of the tweet being replied to, a very common refrain is something along the lines of “national polls are meaningless, the states elect the President, not the national popular vote.” This contains a kernel of truth, given a choice between knowing the result of the National Popular Vote (NPV) and knowing the result of the tipping point state ahead of time, you would obviously choose the tipping point state, that would directly tell you who won the election. But the nation is made up of states, and the incredibly large amount of high quality national polling will probably do a better job of predicting the outcome of the nation than the average state’s polling will predict that state’s polling (though ideally, you’d use a composite of high quality state and national polls to construct a national picture that was somewhere between the two). If you’re smart with how you see national polls, you can take them not just as predictors of the national popular vote, but a tool to plug into priors about the state of states to give you a rough idea of the picture in less polled states. To illustrate that purpose, I want to talk about a very simple model I made using exclusively national polls, and it’s useful as a bit of a gut check to compare to state polls.

An extremely simple polling-based model of United States Presidential Elections

First things first, we need a polling average, I spent a lot of time over the last few months thinking about how to construct these, but that requires some more complicated math, which is against the spirit of this. In addition, no matter what I put together, it won’t have nearly the amount of thought and rigor put into it as FiveThirtyEight’s polling averages, so for our purposes here, we will just treat those national polls as exogenously determined magic numbers to plug into the rest of this model. The actual calculation on our part comes from a very simple process of calculating a partisan lean for each state (plus DC and the Congressional Districts of Maine and Nebraska that get their own detached electoral votes). This is done in this model by simply taking the Democratic Presidential candidate’s margin of victory in that state and subtracting their NPV margin from that number. The fact that the partisan lean is calculated relative to the nation as a whole is important here, doing otherwise would let Obama’s big wins in 2008 and 2012 blind a 2016 model, as a for instance. Do this in every state + DC + the necessary Congressional Districts for the two elections preceding the one one being forecast. Then put together a weighted average for the partisan lean from each state as 75% from the previous election to the one being forecast and 25% to the next most recent one. You can then plug the national polling average into this and see broadly what the national polls imply about the upcoming election.

Sanity Check: What would this say in 2016?

This map here is what that model would say on the morning of election day 2016, with Hillary Clinton leading in the national polls by an average of 3.8 points, and a greyed out tossup state meaning it’s less than a 5-point race, a light shade of lean D or lean R is a race between 5 and 10 points, a medium shaded likely D or R is a race between 10 and 20 points, and solid D or R is a 20-point-or-more-race:

One thing to note here to get into the rest of this, this uses 2008 as the election with the 25% weight for the partisan leans, this was on a different redistricting cycle than 2012/2016/2020. If I felt it was particularly necessary to go into the CDs with electoral votes and look at the specific counties they contain and weighting things that way, but NE-02 and ME-02 are both close enough to their 2000s definitions here that I don’t feel that’s necessary. Now to the substantive issues.

The obvious thing to see here is that Hillary Clinton was favored to win in this, but that’s not an indictment of this approach on its own. Hillary Clinton was favored in all looks at the election that weren’t either completely detached from reality or used exclusively non-polling data as its inputs. Being extremely overconfident in Hillary Clinton is also a bit of a sin, and I think this image here would not display extreme confidence, even a flip from Iowa would’ve cost Clinton victory without winning a tossup or two. The issue here then is the geographic distribution of the electoral votes Clinton was getting here. Iowa was something this model is more confident in than Virginia or Colorado, there is a reason for this, and that is Barack Obama. Obama had a particular strength in the Midwest, he was based out of Chicago, and despite having ample opportunity as the first black President to talk a lot about race relations in very thoughtful ways, Obama focused on things like healthcare that frankly weren’t as challenging to the priors of Midwestern whites. This led to huge margins in the Midwest that gave him an electoral college advantage, Obama could’ve lost the NPV by 1.0 points and still taken the White House. Hillary Clinton, by contrast, based her campaign from New York, and had a campaign largely staffed by younger people who wanted to boost talk about social liberalism, Hillary Clinton was the first major party nominee to actually say “systemic racism” as a for instance. This plays great for urban liberals like me, and she was able to win the NPV over Trump, but it concentrated her votes in those urban areas while failing to stop the bleeding in the suburban and rural Midwest where largely sympathetic to things like universal healthcare, but frankly sort of racist and sexist, white voters rejected a campaign fighting from the left on the cultural issues instead of the economic ones. Thus, Iowa being redder than Texas after being a state Obama carried by more than the NPV just four years prior. All of that being said, this is pretty close to FiveThirtyEight’s projections in 2016, with the exception of Iowa being clearly more right wing and Virginia being clearly more left wing. So some of this is the lack of state polls in this, but a lot of it was that the state polls were also wrong in this sort of direction. I think this actually performed better in 2016 backtesting than I expected, and I think this proves its usefulness as a bit of a gut check about the national state of the race.

What things look like in 2020

Biden is currently up 10.5 in the FiveThirtyEight national polling average. This is what this corresponds to on a map with 2016 weighted 75% and 2012 weighted 25%.

This is pretty damn close to a state poll based model. It’s a bit more bullish on Biden in Florida and bearish on Biden in Arizona than is really implied by the polling in each state. But otherwise this checks out. The tipping points are in the trio of Michigan, Pennsylvania, and Wisconsin, and Biden has a pretty big lead in them. That being said, it’s the same as the leads (well, actually a little more if you look at the spreadsheet, but the same on the map) as Hillary had in the previous model because of assumptions of a Democratic electoral college advantage that turned into a disadvantage. Is this something to note? I think it is to an extent, it’s important to realize that Trump can still win, but it requires both a shift in polling and a roughly 2016 sized polling error in the states that matter again, and given that polls don’t have a predictable direction of error from cycle-to-cycle, that’s a risky thing to have to rely on. I think it’s also worth noting that Biden just has a more stable base of states in his column here, VA and FL are lean D here, along with a tossup Arizona that based on state polls is probably lean D. Being up double digits nationally gives you a lot of paths to victory that are demographically distinct from each other, losing Michigan may mean that you’ll also lose Wisconsin, but it doesn’t say nearly as much about whether you’ll lose Arizona. The other thing to mention here is of course tossup Texas, Trump has generally led polls in Texas, but by a narrow enough margin that a fairly small error could result in a Biden win. Texas isn’t getting the highest quality polls either because it’s both hard to poll and not terribly important for the result of the election, if it’s particularly close in Texas, Biden will have already won. FiveThirtyEight puts the chance of this upset at about 30%, which feels about right. This national polls set up says Biden would need to win the NPV by about 13 points to pull it off, which is within the realm of possibility, if unlikely.

Conclusion

Why did I write this anyway? It feels like I’m making a lot of very banal points. Well, this really was meant as a bit of a response to “national polls are meaningless” people, to show that they can provide a good base level sanity check if other things are leading in confusing directions or just feel too complicated in general.

I might also post updates of the 2020 version of the map if the polls fluctuate noticeably between now and election day, though I should make it clear I make no claims to that being a particularly great election forecast, merely a very simple one that’s easy to understand. But it’s not probabilistic, doesn’t take into account nearly enough information, and assumes the election will be held immediately. Still, it’s useful.

How My New Brunswick Election Ratings Did

I issued NB election ratings based on a quantitative model I privately made, it did pretty well. I talked about the mechanics of how it worked and its strengths extensively in this tweet thread, so I won’t go over all of that again, instead I want to talk about where I failed and what steps I plan on taking on this topic in the future.

I would say I really screwed up in one specific electoral riding, Shippagan-Lamèque-Miscou, a riding with a PC winner previously who defected from the party, but I had it still as Likely PC, instead it went big for the Liberals. I think this suggests that I need to do some amount of demographic regressions in future models to realize that, “hey, Francophone districts will tend to be way more Liberal on average”. That’s a specifically New Brunswick problem, but it’s probably worth carrying over in districts where, say, a Blue Dog Southern Democrat type is retiring and it’s an open seat, in case that ever might be a problem.

In terms of what I want to do moving forward, I was a bit upset with having to rely on someone else’s pollster ratings for this analysis, so the main thing I will be working on is a pollster ratings algorithm, first in America just to get a sense of how to do it in a more data-rich, two-party environment, then applying it to Canada, where the data will be broadly more useful as a public resource in a more sparsely analyzed area.

That’s all for now, but I hope people who do read this blog regularly enjoyed seeing me get things pretty close to all correct in this election. It was fun for me as a (probably not actually that impressive) self-confidence booster.

2020 New Brunswick Election Ratings

I live in the Canadian province of New Brunswick, which just called a snap election thanks to a breakdown in talks between the provincial Progressive Conservative and Liberal parties. The election is happening on September 14th, and it is also the first election in which I will be able to cast a ballot, which has made me pay a greater amount of attention to provincial politics than ever before. This eventually led to me looking at the polling and past election numbers to try to get a sense of where the race stands in each district. This is based on a very basic statistical analysis I did with some polling data that recently came out where I constructed a partisan lean for each district and ran the province wide polling numbers for that. Here are the results, they’re subject to change with new polling data and updates to the way I’m evaluating things:

Safe Progressive Conservative

  1. Riverview
  2. Albert
  3. Gagetown-Petitcodiac
  4. Carleton
  5. New Maryland-Sunbury
  6. Saint Croix
  7. Kings Centre
  8. Saint John Lancaster
  9. Portland-Simonds
  10. Saint John East
  11. Rothesay
  12. Quispamsis
  13. Hampton
  14. Sussex-Fundy-St. Martins

Likely Progressive Conservative

  1. Shippagan-Lamèque-Miscou
  2. Southwest Miramichi-Bay du Vin
  3. Moncton Northwest
  4. Moncton Southwest
  5. Carleton-York
  6. Fredericton West-Hanwell
  7. Fundy-The Isles-Saint John West
  8. Oromocto-Lincoln-Fredericton
  9. Saint John Harbour

Lean Progressive Conservative

  1. Carleton-Victoria
  2. Fredericton-York
  3. Fredericton North

Tilt Progressive Conservative

  1. Moncton East

Progressive Conservative Total: 27

Safe Liberal

  1. Edmundston-Madawaska Centre
  2. Victoria-La Vallée
  3. Restigouche-Chaleur
  4. Bathurst West-Beresford
  5. Bathurst East-Nepisiguit-Saint-Isidore
  6. Caraquet
  7. Kent South
  8. Shediac Bay-Dieppe
  9. Shediac-Beaubassin-Cap-Pelé
  10. Dieppe

Likely Liberal

  1. Madawaska Les Lacs-Edmundston
  2. Campbellton-Dalhousie
  3. Tracadie-Sheila
  4. Moncton Centre

Lean Liberal

  1. Restigouche West
  2. Miramichi Bay-Neguac

Tilt Liberal

  1. Moncton South

Liberal Total: 17

Safe Green

  1. Fredericton South

Likely Green

  1. Kent North
  2. Memramcook-Tantramar

Green Total: 3

Likely People’s Alliance

  1. Mirimachi
  2. Fredericton-Grand Lake

People’s Alliance Total: 2

This looks much nicer than an earlier version of these ratings I had, which were far too overconfident in what counted as “safe”. Here’s a map of the state of the race right now. Blue = PC, Red = Lib, Green = Green, Purple = PA, and White = Tossup. Things are still subject to change as new polling comes in.

UPDATE 2020-08-31: Added a new poll into formula for determining race. A couple districts changed ratings (Edmundston-Madawaska Centre and Kent South from Likely to Safe Liberal and Kent North from Lean to Likely Green).

UPDATE 2020-09-07: Victoria-La Vallée changed from Lean Progressive Conservative to Safe Liberal after the PC candidate got dumped from the ballot for doing a transphobia.

UPDATE 2020-09-11: Only a few days before election night now and I’ve updated the model to include some additional polls that came out.

UPDATE 2020-09-13: What I imagine is the last update before the election barring a last minute poll, the model has been updated to reflect this EKOS poll, and all toss-ups have been renamed to “Tilt [some party]” so that I have a stated take on which way each and every riding will go prior to election day.

UPDATE 2020-09-13 PART 2: This is probably the final update, taking into account the results of one Mainstreet Research poll that came in right under the wire.

2020 Blue Jays Blogging, Game 6: 2020-07-29 @ Washington Nationals

The Jays lost a game 4-0 in ten innings in a game that was scoreless until there were two outs in the top of the tenth. The extra innings loss was rough, but that’s not what we’re really here to talk about in general today, we’re here to talk about the man who made his Major League debut today, Nate Pearson. Nate Pearson was the top prospect in the Blue Jays system, and the #8 overall prospect according to FanGraphs. The man is 6’6, throws 100 mph, and did great today.

Pearson has a mix of 4 pitches, the aforementioned fastball, which sits at about 96, and he has some issues with commanding it that you might expect, though he got much better on that as the game went on. His other great pitch is a hard slider, upper 80s, it has the movement of the super hard sliders that almost have more of a curveball movement where they break down vertically instead of across horizontally. He also worked in a changeup and curveball occasionally, but they’re clearly meant as a change of pace, working because they are rare and unexpected rather than particularly great on their own. Pearson went 5 scoreless innings, walked 2 and struck out 5. As mentioned, the fastball command was shaky earlier, and a man did reach third at one point, but for the most part it was smooth sailing, and a slowly building level of confidence from a great looking rookie. I don’t really have too much in detail to say about Pearson right now that I won’t get the chance to talk about later, and this game ran a bit late, so I’ll sign off on a short article with this highlight.

2020 Blue Jays Blogging, Game 5: 2020-07-28 @ Washington Nationals

The Blue Jays won a pretty comfortable victory 5-1 today, moving their record to 3-2 on the season. The first of these runs was via a solo shot from Vladimir Guerrero Jr., a 21 year old former #1 overall prospect and my favorite active player in sports, it’s time to talk about Vlad.

Vladimir Guerrero Jr. is of course the son of Vladimir Guerrero, the Hall of Fame outfielder who played primarily for the Montreal Expos and Los Angeles Angels. However, Vlad Jr’s hitting profile is very different from his father, Jr is more reliant on stronger raw power and plate discipline than Sr’s otherworldly contact ability. In 2018, Vlad was hitting .400 for much of the year in the minors. There were calls all over to call him up as the big league team struggled, but the front office kept him in AAA to manipulate his service time as to make sure he would be a Blue Jay through 2025 instead of becoming a free agent after 2024, this is obviously ethically bleh, but this is on the league collectively to solve, front offices doing this are just doing their job as assigned by owners. Then, in 2019, Vlad was finally called up, two years of pent up frustration at an old and stagnant Blue Jays team getting its look at the face of its future. And he was, uh, alright. He was fine. He was an above average hitter with a 105 wRC+, but that wasn’t the guy people had hyped him up for, and holy shit was that defense at third base horrendous, is this kid a bust?

Okay, step back, the guy was 20 years old. The number of 20 year olds or younger who have above average hitting seasons in the Majors is very exclusive, this is an exhaustive list of people who have done it in the 21st century (minimum 100 PAs):

All of these people became extremely accomplished big leaguers, if the worst case comps here are Justin Upton and Jason Heyward, I will gladly take that. The defense, and Vlad’s general flab are somewhat concerning, but he should be fine at first for at least a few years. This guy hits for pretty good average, has raw power only really challenged by Judge and Stanton, and he could have the potential to take on some of their game power if he can hit more balls in the air. This kid is alright, let’s enjoy him as much as we can.

2020 Blue Jays Blogging, Game 4: 2020-07-27 @ Washington Nationals

The Blue Jays won tonight by a margin of 4-1, with all the runs being scored via solo home runs from some members of the Blue Jays supporting cast, so let’s talk about that supporting cast.

Danny Jansen is the Blue Jays’ starting catcher, succeeding Russell Martin after he was traded to the Los Angeles Dodgers in the 2018-19 offseason. He has been somewhat disappointing on offense, sporting a horrendous 68 wRC+. And yet he was still reasonably valuable, racking up 1.4 fWAR in 2019, what gives? Well, catcher defense is obviously quite important, and a part of that that was overlooked for a long time was the framing of pitches, making balls look like strikes by cleverly adjusting where pitches seem to go in the view of the umpire. Doing this well can be tremendously valuable, as it comes into play in practically every plate appearance. A very good framing catcher can add something like 10-15 runs above average in a year, and that’s what Jansen did. In better news, that 68 wRC+ is pretty anomalous with the rest of his career, so he can be considered likely to do a bit better than that this year (see the home run today). All of this and he’s only 25, Jansen is a quite undervalued part of the team.

Second is Teoscar Hernandez, someone with a much more complicated value profile. Since he came to the Jays in late-2017, he has looked at times like a great power hitter, and at others like an infuriatingly bad swing-at-everything guy. This averages out to a slightly above average hitting performance. This combined with some terrible outfield defense in 2018 made him essentially a replacement level player. Thankfully, after a stint in the minors in 2019, he seems to have made his outfield defense go from horrendous to acceptably below average. That makes him worth a win and change. Granted, he’s 27, so this is presumably his peak form, but I’ll take it. Especially if him hitting 2 home runs today is a sign of anything to come here.

Finally, Rowdy Tellez, maybe the least interesting guy on the list. The guy is a one dimensional power hitter who doesn’t get on base very often, but when he does, expect it to come from a home run or hard hit line drive. He’s essentially an average hitter who is mostly a DH, not much to say about him, other than that in this juiced ball environment, 20 home runs will come easy to him. This is almost certainly just Who He Is.

Before closing this one off, it’s worth noting the full-on COVID-19 outbreak that took place on the Marlins earlier today, it seems quite likely that this season isn’t going to end in a way that people would like it to. But who knows, maybe things actually manage to go through. Nobody, clearly even the people in leadership positions at MLB, knows what’s going to happen. I’m just gonna be blogging whatever Blue Jays games happen in this crazy year.

2020 Blue Jays Blogging, Game 3: 2020-07-26 @ Tampa Bay Rays

As the ninth inning of today’s game was coming to an end, I was outlining in my head an article that would talk about Bo Bichette’s successes as a prospect and a young Major Leaguer. Then Some Things happened, the Blue Jays lost, and today we’re gonna be talking about bullpen management.

First of all it’s worth looking at this win expectancy graph:

Win expectancy is a metric that takes into account the score, inning, number of outs in the inning, and who is on base, to put together a composite of how often games with states exactly like that went (ignore the grey line, that’s just FanGraphs’ version of win expectancy that takes into account the fact that the Rays are a better team than the Jays). You will notice looking at this graph that the Blue Jays were in a very safe situation, and managed to very improbably blow that lead. They were up 2 runs with nobody on base in the bottom of the ninth with 2 outs. Something had to go extraordinarily wrong here. That thing was Ken Giles’ elbow.

“Elbow soreness” is a terrifying diagnosis in itself given the Tommy John risk, but let’s just focus on the impacts it had on the game. Ken Giles stopped being able to throw nearly as hard as he generally does, and he was no longer able to hit the strike zone at all. He was eventually pulled, but not before walking the bases loaded and ultimately sealing the fate of the Blue Jays to lose today. He should’ve by all rights been pulled earlier, and the previous two articles of this I’ve complained about manager Charlie Montoyo’s bullpen management, so let’s get into it.

There are three fundamental things you need to know about bullpen management: The times through the order penalty, the Leverage Index (LI), and the save situation. The save situation is the easiest to explain and is the most oft known, so let’s explain it first. The save is a counting stat that applies in these situations:

The save in and of itself is a sort of innocuous counting stat, but it becomes actively dangerous when managers manage around that instead of doing what will help his team win games as much as possible. For instance, many teams have a very rigid Closer, their job is to come in in the ninth inning (or eighth inning if the guy working that inning is in deep shit) with a lead of three runs or less and hold it down to get the save. This Closer is not allowed to go in earlier, when the game is more truly in the balance. This Closer can also not be replaced in a save situation with someone who might be better suited to the particular batters coming up. The most famous instance of managing to the save backfiring was actually something the Blue Jays were on the winning end of. In the 2016 American League Wild Card Game, Orioles manager Buck Showalter refused to face the heart of a very dangerous Blue Jays lineup with his closer Zack Britton, WHO HAD JUST SAID THE SINGLE SEASON ERA RECORD FOR A RELIEVER, because the game was tied, and as such allowed starter-in-relief Ubaldo Jimenez let this happen when Britton could merely watch from the bullpen.

In recent years, smarter managers like Kevin Cash of the Rays have shifted away from this idea, in favor of bullpen management through the two other factors I mentioned, LI and the times through the order penalty. LI is merely a measurement of how important a moment is to the outcome of the game, measured as a multiple of 1.00 where 1.00 is a completely average plate appearance. Smarter managers have tended away from having a closer to just having “highest leverage relievers”, Jose Alvarado of the Rays is a good example of this, he generally comes in in the late innings, but only because those are higher leverage, he can come in the eighth or in extras, and he can also come in when the game is tied. Finally the times through the order penalty is simply the fact that when hitters face the same pitcher subsequent times, they do better each time. This is an important fact to keep in mind with regards to pulling starters earlier, but it’s not particularly important for my insulting of Montoyo here.

Montoyo has in each of the three games this year not been aggressive enough in managing, being too much of a stickler to rigid roles. He left Rafael Dolis in for too long in game 1 but wasn’t punished by that decision, he put Sam Gaviglio in a high leverage situation in game 2 despite him being suited more for long relief work and mop-up duty. And today, he left Ken Giles in after his velocity readings made it clear something had gone really wrong, because dammit he’s The Closer, and The Closer should get to live and die in a save situation. Managers do so much of their work behind the scenes in a way that’s impossible to know how effective they are at that, so I have no idea if Montoyo is effective enough to fire if at the end of this season this team loses a lot to bad bullpen decision making. But that bad bullpen decision making is real, and it’s especially disappointing when Montoyo came from the Rays, the team that provides the gold standard of bullpen management. Facing that team proved too much for their ex bench coach.

2020 Blue Jays Blogging, Game 2: 2020-07-25 @ Tampa Bay Rays

To put a damper on the Blue Jays’ upset victory last night, the Rays won game 2 4-1 to even the series ahead of its finale tomorrow. The game was in a 0-0, then 1-1, tie for most of its runtime, but Sam Gaviglio gave up 3 runs in the 8th. The questionable bullpen management of putting a long reliever like Gaviglio in a high leverage single-inning aside, I want to focus on the bright spot of this game. Matt Shoemaker.

Matt Shoemaker was an undrafted free agent (for reference, the MLB draft is typically 40 rounds, undrafted free agents making the bigs are incredibly rare) in 2008, and after 5 years in MiLB, he made his MLB debut for the Angels in 2013. In 2014 he had a great rookie season, but suffered a rib cage injury that sidelined him for much of the year. In 2016 he took a comebacker to the skull and was out for the season. In late 2017 he came back, and in 2018 he started to get back to things, and then he had to get forearm surgery. Finally, in 2019 with the Blue Jays, he was great in 5 starts before tearing his ACL. This is a roller-coaster of a career, but he’s been quite effective when he’s actually had the chance to pitch, so let’s look at what that pitching is like.

Shoemaker has decent but below-average velocity. The man has to rely on his 5-pitch arsenal. Those are a 4-seam fastball that he mostly threw high in the zone when he was behind in the count. He also has a splitter and a sinker that together with the 4-seamer make up most of his arsenal. He also has a slider he occasionally pulls out as a swing-and-miss pitch, but the real oddball of his arsenal are his occasional curveballs that MLB’s database actually calls knuckle curves.

The only real giveaway as to these pitches’ true identities for me was to look at their spinrates. A normal good curveball has an RPM of about 3000, this pitch was at about 2000. Either way it’s an interesting novelty that isn’t used that much regardless.

So that’s Matt Shoemaker, a pitcher who is quite effective when he’s healthy thanks to the diversity of his fastballs, he just unfortunately isn’t often healthy. Hopefully the Blue Jays can get a good start tomorrow that isn’t squandered by the lineup and bullpen.

2020 Blue Jays Blogging, Game 1: 2020-07-24 @ Tampa Bay Rays

https://i.imgur.com/tlA1s0W.png

Welcome to the first game of the season fellas. The Blue Jays won a frequently nerve-wracking game in Tampa 6-4 to put themselves at 1-0 for the season. Three of those runs were scored by this blast from Cavan Biggio.

So as I spend the first bits of the season getting to write about the Blue Jays in depth for the first time, I should talk about their key players. Today, that key player is Cavan Biggio.

Cavan Biggio is most obviously notable for his parentage. His father, Craig Biggio is in the Baseball Hall of Fame and is the very model of a modern major baseball player. He had positional versatility, he had great plate discipline (and a knack for crowding the plate for getting crazy hit-by-pitch totals) he played good defense when asked to, and had reasonable power and good speed that he made very smart use of. He was really good at every aspect of the game while being Truly Great at none of the super obvious parts to create a whole greater than the sum of his parts. I mention this because Cavan Biggio has some of this mentality. Cavan Biggio has run pretty consistently unimpressive batting averages in his career in both the minors and majors, but in 2019, he was a 24 year old rookie who had the fourth highest walk rate in MLB (minimum 400 PAs). He took some strikeouts looking for this, and probably sacrificed a bit of power, but it bumped his OBP to a genuinely impressive .364, higher than Pete Alonso’s (this isn’t to say Pete Alonso was worse, obviously you take 50 home runs for a slightly worse OBP if you can get 50 home runs). He’s posted roughly average second base defense in his one season, and he’s a threat to steal despite not being particularly fast. Biggio is a recent rookie like the two other core members of the Blue Jays hitter core, but he’s older and shows it with the level of maturity and discipline with how he plays, even if that comes at the cost of the pure athletic talent of Guerrero and Bichette, who we will talk about in future articles.

Also worth noting that this was the Blue Jays debut of Hyun Jin-Ryu, who signed for $80M over 4 years in the offseason. He wasn’t particularly great today after allowing a home run in the fifth, but he had pieces of what makes him good today, he just didn’t quite have the control that he usually does, with a very uncharacteristic 3 walks. Finally, Rafael Dolis was allowed to fester in a high leverage situation despite performing poorly in a way that put me off. The Jays happened to escape the jam, but things could’ve easily gone off the rails there if a hanging slider to Yandy Diaz wasn’t just turned into a fly-out. I expect managers to manage more aggressively in a season where every game is as meaningful as this one, especially for an on-the-bubble (thanks expanded playoffs!) team like the Blue Jays, Charlie Montoyo didn’t perform to that expectation. Not a huge deal for now, but if it turns into a consistent trend it could be worrying.

See you tomorrow for game 2 against the Rays, Matt Shoemaker gets the start for the Jays after a good start to the season in 2019 was killed by an ACL tear, looking forward to seeing him.

An Attempt At Something

I’ve written a decent bit on this blog about Major League Baseball, most notably with a 10000+ word essay on a specific Max Scherzer season. But my MLB writing hasn’t been about my favorite team at all. Today that changes, despite having written a massive piece on a Nationals player, I am a Blue Jays fan. The Blue Jays will not have access to their home ballpark over this 60 game season, meaning that they’ll be perpetually on the road in some way. Combine that with the shorter season and bizarre expanded postseason format MLB is offering up this year, I want to write about the Blue Jays’ experiences this year game-by-game from an analytical perspective here. I will try to catch at least most of all 60 (and possibly more) games the team plays, and I will try to talk about something interesting from each of them. This might fall flat on its face, but I trust I can push a few hundred words a day if pushed. I’m excited about MLB returning after a few months of trying to ignore it because of how sad its delay was, and I really think this is going to be a useful writing exercise. See you tonight after Game 1 of 60.