Reddit mentions of Analyzing Baseball Data with R (Chapman & Hall/CRC The R Series)

I'm usually pretty optimistic for people when it comes to posts asking about "how do I get started in sabermetrics" because I was in that position once as well, and it's worked out okay for me, but I want to be a bit more realistic, because I think there is a big red flag that you should recognize in yourself in respect to this.

There are a couple ways to get jobs in fields that require sabermetrics, but you should be aware: there are very few, they are highly competitive, and they require a good amount of work.

The traditional progression for doing sabermetric work is usually something like:

Stage|Level of Sabermetric Experience|Work you're qualified to do|
--:|:--|:--|
1|You look up stats online to form arguments about baseball|Personal blogging, entry-level analytics writing (FanSided, SBN, other sites)|
2|You put stats into a spreadsheet to visualize data or calculate something new to form an argument about baseball|Personal blogging, entry-level analytics writing (FanSided, SBN, other sites), heavier stuff if you're very lucky and a good writer (bigger sites like FanGraphs, Baseball Prospectus), general baseball coverage that isn’t heavily analytical|
3|You use code with baseball stats to visualize data or calculate something new to form an argument about baseball|Heavier analytics writing (SBN, FanGraphs, Baseball Prospectus, The Athletic), entry-level baseball operations work|
4|You use code to create your own models, predictions, and projections about baseball.|Extremely heavy analytics writing, baseball operations/team analytics work|

From your post, it sounds like you're somewhere between #1 and #2 right now. However: "after trying [coding] out I did not like it." You have a very large barrier keeping you from making the jump to stage 3.

If you actually want to go into a sabermetric field as a career, you need to know how to code. Not with Javascript, mind you, but other languages (Python, R, SQL, etc.). I would advise that you try out Python or R (Analyzing Baseball Data with R is an excellent introduction and gives you a lot of practical skills) and see if those really suck you in - and believe me, they need to suck you in. If you really don't like it, don't force yourself to do it and find some other career path, because you won't be able to succeed if you can't enjoy the work that you do.

FanSided has very low barriers of entry and the compensation reflects that - you cannot make a career out of blogging for FanSided. Even if you get to where I am (stage 4), if you're lucky, you might land a contributing position at a site that pays decently for part-time work. There are extremely few people who are somewhere between #3 and #4 who can make a full-time living off of baseball work, and they do it because they like what they do - if you don't like coding and working with baseball data in that environment, you're not going to be able to beat out everybody else who's trying to get there.

Let's say that you work your rear end off, you get to stage three or stage four. What options are available to you? There's maybe a handful of people who work in the "public" sector - that is, writing for websites like FanGraphs, Baseball Prospectus, The Athletic - who make enough money to make sabermetrics their full-time job. It will take a hail fucking mary to land one of those jobs, regardless of how talented you are, and you'll basically need to work double-duty on both sabermetrics and whatever your main hustle is until one of those positions opens up, and even then, you're not guaranteed anything.

You could also work for a team! There are far more positions available, they pay better, you have more data to work with, better job security - this sounds great, right? Problem is, the market cap for analysts are at about 20 per team, so there's something like 600 analyst positions that could be available in the future (I can't promise that the MLB will ever have 600 analysts total at any given time, but that's an upper estimate). And almost half of those are already full! There's not a whole lot of brain drain from the industry, so it is still extremely hard to break in and you're still going to be competing with the absolute best people in the industry. You will have to love to code and do this work because everybody you're competing with already does, and everybody else is willing to work twice as hard for it.

My advice to you is this: try out R or Python with baseball data. See if it's enough to get you addicted. See if it starts to occupy every ounce of free time you have, and you feel comfortable with it, and you're willing to put yourself out there and advertise your own work. I'm a full time student and basically every ounce of my free time is put towards working with this stuff, like it's a second full-time job for the past three years, and I'm still a bit of a ways away from making a living off of this. If you can't learn to love it, your time and energy are best spent elsewhere.

Analyzing Baseball with R is the best book, I believe:

https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229

I also would download PitchRX and Baseball on a Stick to round out your toolkit!

-Kyle

Analyzing Baseball Data with R

https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229

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Walks you through learning the program using baseball stats as the foundation.

https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229

This book covers everything related to how to get the data (Retrosheet, Lahman's, pitchf/x IIRC) and then how to do a lot of different stuff with R. It's a good place to start. You could probably find it cheaper than that Amazon link though.

https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229/

It's an introduction to baseball data, statistical analysis, and the R programming language.

Sure! I'll just assume knowledge of the more common stuff like OPS. I'll try to break it into learning resources v. interesting work to be read. Think my suggestions to OP might be structured a bit differently. I'll try to keep it moderately short.

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Learning

The Book: Playing the Percentages in Baseball set the foundation for a lot of stuff seen today. Win expectancy, lineup optimization, "clutch" hitting, matchups, etc. A lot of it is common knowledge today, but probably because of this work. It's great to see them work through it.

This is a bit of a glossary to many of the more important stats, with links for further reading.

As well, not quite the same, but Analyzing Baseball Data With R is also a great introduction to learning R, which is probably preferable to Python for a lot of baseball-specific work (not to make a general statement on the two, at all).

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Reading

A lot of good work is, somewhat annoyingly, scattered through the internet on blogs. I don't have time to dig up too much right now but I'll shamelessly plug some work a couple of friends did a few years ago that was rather successful. These are mostly just examples of the what these projects tend to look like.

• The Value of Draft Picks
  
• Projecting Prospects' Hitting Primes
  
• xxFIP p1 p2 p3
  
  Much of the more current work will probably be found on FanGraphs' community submissions section, which I honestly haven't up with recently. I imagine a lot of focus is on using all the new Statcast data.
  
  There's also the MIT Sloan Sports Analytics Conference, where a lot of really cool work comes from. The awesome part about Sloan is that there seems to be a strong emphasis on sharing; I looked for the data/code for two papers I was interested in and ended up getting it for three! My favourite work might be (batter|pitcher)2vec. This is more machine-learning oriented, which I think is a good direction.
  
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  That's all I have time for rn, hope that helps!

Here you go! It's very helpful and has a wide range of topics so you can learn whatever you want. It uses Retrosheet, Lahman and Pitch Fx

https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229/ref=sr_1_1?ie=UTF8&qid=1494296330&sr=8-1&keywords=analyzing+baseball+data+with+r

I'm in a similar boat as you. I'm a biologist by trade, but want to delve deeper into statistical analysis with R programming to add a new skill to my career. I'm also a huge baseball fan, especially love it for the stats.

A friend of mine gave me this book for a birthday gift and I've been working way my through it, albeit very slowly. So far (I'm only at Chapter 3), it's been easy to follow and a nice to guide through R. I'd suggest it.

The edx course, that /u/sin7 suggested sounds interesting as well.

Mostly because I wanted to analyze baseball stats, and at the time (4-5 years ago) that was mostly done in R. If the last industry conference I went to is any indication, it still is, many of the presentations features plots that were clearly ggplot2. There are also books like this one floating around: https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229/ref=nodl_.

You might find some answers in these books:

http://www.amazon.com/Analyzing-Baseball-Data-Chapman-Series/dp/1466570229

http://www.amazon.com/Baseball-Hacks-Analyzing-Winning-Statistics/dp/0596009429/

The second author has a website with some starter information:
http://www.oreillynet.com/pub/a/network/2004/10/27/baseball.html

https://www.amazon.com/Analyzing-Baseball-Data-Chapman-Hall/dp/1466570229