mrpickem
SDQL Badass Stat Boss
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2021 MLB Betting Primer
Give me your tired, your poor, your huddled masses yearning to breathe free.
Lady Liberty of MLB is calling for your butthurt NBA bettors screaming it's rigged. For your busted brackets. For the NHL cult members. For the NFL meatheads who are sick of talking about Dalton.
Welcome to MLB 2021!
This primer serves as an introduction to general MLB betting thoughts you should consider, statistics, ways to follow the league, and other things I find interesting.
- Unlike most sports, the lineups change every day so it is pretty important to check the lineup/starter.
- Likewise, not many sports outside of football and baseball have weather effects. Air Density has different effects on the flights of baseball. "The numbers that I have found just by looking at the data, are that a 10 degree Fahrenheit change in temperature will change the distance by something like 2.5 feet." http://www.accuweather.com/en/weather-news/changes-in-air-density-can-aff/28805375
- Also, there are park factors. Some parks are pitcher friendly (AT&T, Dodger Stadium, Marlins Park) and others are hitter friendly (Coors Field, Fenway, Chase Field) Source using 2020 data Park Factors. Also, keep in mind that TEX has a new park, and that there are more humidors in use this year than ever. AND a new baseball which is more deadened.
- Bullpens sometimes suck (Angels 50(!!) MD Meltdowns) and can ruin a lot of bets by blowing a game. Consider this and consider a 5-inning line. A good place to check bullpen fatigue is here: Bullpen Usage - Daily Baseball Data
- A big part of betting is considering the two matchups that are going on the whole game. Home team Offense vs. Away team defense and vice versa. They are two pretty distinct matchups since lineups are made to neutralize an opposing pitcher using platoon splits.
Everyone has pretty much heard of Moneyball and SABRmetrics, but this is an attempt to simplify the complexities of advanced analytics into something that is much easier to understand.
Background: In the old days, Wins for a pitcher was considered a great statistic to measure a pitcher by. More wins surely means better pitcher. Then analysts realized that a pitcher can’t really control how much offensive support he gets. ERA measures how many earned runs a pitcher allows per 9 innings, and it's a quick way to analyze a pitcher's ability to suppress opponent offense.
But what if a pitcher has a worse defense which allows more balls in play to be converted into outs? The goal of advanced analysis is to remove luck or other factors, like sequencing, from a pitcher/batter’s performance.
For example, a pitcher can control three things, Walks/Strikeouts/Homeruns. Everything else involves the defense. Fielding Independent Pitching (FIP) is a statistic that uses only those three statistics and is on the same scale as ERA. You can see if a pitcher is getting “lucky” or “unlucky”. That’s the goal of sabermetrics, removing luck/differences.
Below is a list showing sabermetric statistics and some quick points about them. The links to the FanGraphs Library will explain more.
If you spend more than 20 hours a year on FanGraphs you should buy a subscription!
- ERA: Earned Run Average Measures the amount of runs a pitcher allows per 9 Innings Pitched (IP). One of the easiest ways to measure pitchers, but it has its flaws as well.
- FIP & xFIP: (expected) Fielding Indepent Pitching. Measures what a player’s ERA would look like over a given period of time if the pitcher were to have experienced league average results on balls in play and league average timing. These are on the same scale as ERA so it is easy to compare
- WHIP( WHIP | Sabermetrics Library): (Walks+Hits)/IP Quick measure of how many base runners a pitcher allows per inning 1.00 or less is Excellent, 1.32 is avg, 1.60+ is Awful
- GB%, LD%, FB%(GB%, LD%, FB% | Sabermetrics Library): Ground ball%, Line Drive%, Fly Ball%. This measures the types of balls in play a pitcher is allowing (or a hitter is putting in play) Batters hit .685 on line drives, .239 on grounders, .207 on flyballs. Flies have the highest power though.
- BABIP( BABIP | Sabermetrics Library): Batting Average on Balls In Play. Take all the strikeouts and HR out of a pitcher (or batter) stats and look at the Hits/AB. Typically around 30% of all balls in play fall for hits, but there are several variables that can affect BABIP rates for individual players, such as defense, luck, sprint speed, and player talent profile. Hitters have more control over their BABIP than pitchers do.
- Plate Discipline( Plate Discipline (O-Swing%, Z-Swing%, etc.) | Sabermetrics Library): Click Link for more Detail. Measures the abilities of batters/pitchers to judge pitches during an at bat. Useful for strikeout totals on certain pitchers/teams.
- HR/FB%(HR/FB | Sabermetrics Library): Homeruns allowed per fly ball allowed. HR/FB is very important because it offers insight into how “lucky or unlucky” a pitcher’s home run rate might be. Home runs kill pitchers, but because they’re a relatively rare event a few lucky or unlucky moments one way or the other can dramatically alter a pitcher’s season. League average is around 10% and true talent for almost every pitcher is about 8-12%.
- wOBA: weighted On Base Average. One of the most important and popular catch-all offensive statistics to measure a hitter’s overall offensive value, based on the relative values of each distinct offensive event. Not park adjusted.
- OPS & OPS+: On-Base% + Slugging%. Quick/easy way to analyze a hitter’s performance. Many sabermetricians don’t like OPS because it treats OBP as equal in value with SLG, while OBP is roughly twice as important as SLG in terms of its effect on run scoring. For a quick gauge of talent, this is fine to use.
- wRC & wRC+: weighted Runs Created(+). This is another “catch all” statistic for hitters. For example, a 125 wRC+ means a player created 25% more runs than a league average hitter would have. A wRC+ of 80 would mean a player is 20% worse than league average. This is adjusted for park factors. It is a rate-based statistic, so playing time is not factored in. The wRC is factoring in playing time.
- ISO: Isolated Power. Slugging%-Avg – This is a measure of power. .250=Excellent .060=Awful
- K% & BB%: K/PA & BB/PA. Measures a hitter’s tendencies to strike out and walk. Works for pitchers too. Awful=30%K 4%BB – Excellent=10%K 15%BB. These are typically seen as better than your typical K/9 and BB/9 since they more accurately measure dominance. A pitcher who has a high K/9 might just get a lot of Ks per Inning, but is also putting runners on base. Look into K-BB% as well! Very good for assessing total dominance of a pitcher in one stat.
- Soft%, Med%, Hard%: Measure of Exit Velo. This measures the exit velo for (hitter) or against (pitcher): Someone who hits more hard balls over a large sample size will see more success than a player who doesn't. Consider looking a player's exit velo numbers on baseball savant to get an idea of their Average and their max exit velo.
- EV is Exit Velocity. This is similar to soft medium and hard percentages. Peak EV is around 120mph but only a few players (Judge, Stanton) can get there. Hard Hit % according to Statcast is the % of balls hit at 95+ mph. Below 95 mph typically don’t yield good results.
- WAR: Wins Above Replacement. Some consider it the holy grail of baseball statistics. Incorporates every aspect of the game (pitching, defense, running, batting). MVPs are at 8+, All Star 5-8, starter 3-5, League Average 2, Bench Player 0-2. Since there are 162 games, it doesn’t do much for a single player for a single game.
I tend to focus more on K% and BB% than K/9 and BB/9 statistics. K% for batters stabilizes pretty quickly which is nice. Stat cast numbers are also very good.Here's a good link of how to evaluate a player How to evaluate a hitter, sabermetrically
Statcast & MLBAM
What is MLBAM?
- "MLB Advanced Media (MLBAM) is a limited partnership of the club owners of Major League Baseball based in New York City and is the Internet and interactive branch of the league."
- Statcast is a way to get statistics via video analysis. MLB owns statcast and they measure statistics such as Exit Velo, Launch Angle, Pitch Speed, Spin Rate, Sprint Speed, Bolts, Spin Efficiency, Outs Above Average (OAA), xwOBA, xwOBACON, etc.
This stuff might not be the most useful in single game prediction, maybe if a pitcher has a high exit velo allowed, but it's good for knowing player profiles which can help with DFS, Fantasy, player props, and even lineup evaluation.
FAQ
Q: Where do I get data from?
A: Personally, I've got everything I need from FanGraphs, Baseball Reference. Brooks Baseball, Baseball Savant, and Pitchers List.
It's a great source for numbers on exit velo, launch angle, xStats, spin rates, pitch velo, extensions, defensive shift numbers, player rankings and visualizations. They have also posted matchup previews for games now.
I know people who use Batter vs. Pitcher (BvP) statistics also find those on ESPN (I just google the team/pitcher they're facing). I personally don't find the sample size of these statistics to be meaningful unless you're weighting them with similar players which is too much work for me.
Q: How can I start to make a model?
A: Understanding what certain statistics measure. How you plan to use those to forecast/predict the outcome of a game, whether its the ML or o/u. Automating your model to get all of the data in one place for you eliminates you making an error and also is much faster.
Q: How do I know what game to bet?
A: Every single ML has an implied probability. Lets say we have Yankees -220 and Orioles +185 for a game. The Yankees ML implied a -220/(-220-100) = 2.2/3.2 = 68.75% chance of winning. The Orioles ML implies 1-185/(185+100) = 1-1.85/2.85 = 35.08%.
Together, we see these sum to 103.8%, meaning the juice or vig for this matchup is 3.8% (not bad for a fictional game).
Your model (or gut if you go that route) should tell you a projected win% for each team. If you have the Orioles winning 40%, then you have +EV (positive expected value) on them. You can use the Kelly Criterion to help determine your bet size, although half-Kelly or quarter-Kelly are popular too.
You can google ‘pythagorean win loss MLB’ to get the formula for converting runs scored/allowed to winning %. It usually takes the form of: Win% = RS^2 / (RS^2 + RA^2). However, the most accurate exponent is 1.83.
Q: How can I learn more about baseball, learn rosters, or keep up with this 'crazy' 162 game schedule.
A: Play video games, fantasy (not DFS), watch games to learn rosters. To keep up with news just follow fantasy news, or listen to any podcasts regarding baseball. **Links to podcasts below. The name links take you to their author page on respective websites.**
Personal favorites:
- Fangraphs Audio by the various writers of FanGraphs.
- Effectively Wild- Ben Lindberg of The Ringer, Sam Miller, and the aforementioned Meg Rowley donate to them here Effectively Wild is creating a baseball podcast | Patreon
- For fantasy I like The Sleeper and the Bust hosted by Paul Sporer of FanGraphs feat. guests like Justin Mason who also runs Friends With Fantasy Benefits (which also has their own podcast)
- The Pitchers List podcastis also a good one, as is their website
- The Statcast Podcast is now known as Ballpark Dimmensions. It is hosted by Matt Meyers and Mike Petriello
- Also, STRONG recommendation to follow MLB Sr. Data Architect Tom Tango aka TangoTiger and Co-Author of The Book (blog linked)
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