When assessing starting pitchers, savvy fantasy players look at a wide variety of measures. Velocity, stuff, BABIP, Statcast, 2019 performance…balancing out all of the available metrics to determine cost (draft slot, $ value) is the name of the game.

Today we’re going to look at a metric I rarely see discussed in the pre-season: strength of schedule (SoS). In-season, SP matchups are gold, whether you’re playing DFS or streaming in season-long. But before the year, I rarely see analysis go any deeper than AL-vs.-NL comparisons. This makes partial sense because we don’t know what a rotation will look like beyond the next week, making projecting out specific matchups impossible.

At the team level, however, we can get get a pretty good handle on who may have advantageous matchups and who will have a tough road in front of them. More specifically, we’re interested in the extremes: How frequently will each team face really tough matchups, or really easy ones? These are actionable (start/sit decisions). For the rest – the fat part of the bell curve – we’ll mostly be making decisions based on individual SP talent, not matchup.

One other note: in a 60-game season, each SP only gets 10-12 starts, meaning SoS will be more important than normal. In a reduced season, there isn’t time for the schedule to balance out. If a Rays pitcher has to face the Yankees three times, that’s 25-30% of their 2020 season stats, and you may want to downgrade them on draft day.

I’m basing this analysis on the proposed breakdown of the 60-game schedule found on MLB.com:

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A couple of weeks ago, I took a look at hitters who are being priced cheaper in 2020 than their 2019 stats would dictate. This week, it’s time to assess Starters using the same approach.

Recency bias suggests that 2019 performance weighs most heavily in our minds when making 2020 decisions. That certainly plays out in many scenarios, but there are other players who’s 2020 price is discounted compared to what just happened. I’m guessing that’s mostly due to the prevalence of projection systems in player valuation. A good projection system should absolutely be the baseline for your 2020 valuations. But as we know, these systems are slow to pick up on skill changes. Three year weighted averages & regression to the mean helps the systems get the most players right; but it also means they systematically devalue 2019 stats. The goal of this post is to look at what just happened (2019 performance) and find places where the market (ADP) isn’t pricing in those stats.

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If you had just one stat to use for your drafts this year, what would it be?

A common complaint I see from fantasy experts is recency bias, that cognitive bias whereby we depart from the most rational decision based on an over-reliance of the most recent data because it’s fresh in our minds. Most of us are aware that this bias exists, and try to counterbalance. We use 3-year weighted projections; analyze exit velocity and launch angle instead of RBI; and pay more for a young player with perceived “upside”. In my view, there’s a danger amid smart fantasy owners of going too far the other way and discounting what just happened. Today, I want to take a look at the way a brand-new fantasy owner might answer my initial question: who played the best in 2019?

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After assessing starting pitching the last couple of weeks (ERA, WHIP), today I turn my attention towards the hitting side. There are so many unknowns right now about the length of the upcoming season; possibilities include everything from no games, to a full 162-game season stretching until Christmas. With at bats & counting stats completely up in the air, evaluating hitters with rate stats makes sense. What are the best ones to use?

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Last week, I introduced the goal of this series: utilizing data visualization to try and narrow in on fantasy baseball insights. We looked at ERA across the draft, finding some potential values based on ADP. Today, we’ll take a closer look at Starting Pitcher WHIP by ADP.

To begin with, what’s the context in which we should gauge whether an SP’s WHIP actually helps our team? Here are WHIP trends over the last 5 years:

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There is a LOT of information available for fantasy owners to try and digest these days. New writers and podcasts emerge every day (over 500 different fantasy analysts by last count). New stats and ways of slicing and dicing existing data are constantly emerging. Don’t get me wrong – I love the latest Statcast research as much as the next guy. But fantasy writers often pile up the acronyms and exotic statistics, as if 2000 words on spin rate has inherent interest just because it’s in-depth. It can be hard to find actionable fantasy moves in a table with 10 varying components of xStats.

I’m kicking off a new series today, utilizing data visualization to try and narrow in on fantasy baseball insights. Good visualization helps you achieve your goals by channeling success onto your subconscious until your reality lines up with your drea….I’ve been watching too much late-night Tony Robbins. Good data visualization takes complex raw data and translates it into easily-understood, actionable images.

Please, blog, may I have some more?