In a typical season, after less than two weeks fantasy owners are (trying) to exercise patience. With a 6-month season stretching out in front of us, we have plenty of time to assess changing player skills, see roles slowly evolve, or react to injuries. In this 60-game sprint of a season, all that is out the window. The owner who can assess what’s different today, and react the quickest, will win this season. That may mean cutting a high draft pick, or trading for an unexpected source of power. It all starts by assessing what’s changed in just 12 days.

I’m analyzing Rudy’s pre-season and rest-of-season projections (Razzball/Steamer), with custom dollar values based on a standard roster, 12-team mixed league. The specific dollar values aren’t as important as the relative changes: who’s value has gone up/down, in less than two weeks?

Below, I’ve charted all players who’s value has changed by $2+ since the preseason:

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The great Jeff Zimmerman (Fangraphs, The Process, etc.) recently revisited a topic that’s always ripe for debate: what kind of extra value does a multi-position player get, compared to those who only play one position? We can all agree that multi-position is better than single; quantifying that value, however, proves more difficult. A few years ago, Rudy assessed this briefly in his seminal piece, “Debunking Positional Scarcity“, and recommends adding a $1 for multi-position players.

Jeff’s article took a different approach: instead of measuring what a player’s value should be, he attempted to measure the actual impact in terms of draft cost. In other words, what premium does the market place on these players? Read the full piece; Jeff estimates ~$3.20 bump on average.

While I like the goal (understanding market premiums), Jeff’s methodology (comparing the draft cost of two similarly-projected players) was limited in scope. So I’ve set out to do additional analysis with the same goal: measuring the market premium of multi-position players.

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I believe an overlooked part of draft/auction strategy is finding pools of players that are consistently undervalued. This fits best with a “maximize value” draft strategy, the goal being to add the maximum value onto your team as possible. It can be combined with a “get your guys” strategy, but this analysis is player agnostic.

To do this, I’ve taken to preparing for drafts by analyzing how the market (ADP) prices various fantasy assets as compared to projections. The process:

  • Take the last 3 year’s worth of  data
    • ADP data from NFBC
    • Projected $ value from my home league
    • Translate ADP to $ value by assigning the top player my top $ value, descending (this removes bias from my valuation methodology)
  • Break the pool up into various buckets of players
    • SP bucket, RPs, Hitters
  • Graph the descending values against each other

These value curves provide us a look into how the market prices pools of players, and help us plan how to allocate resources.

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With all the changes to the 2020 season to the 2020 seasons swirling around, I wanted to narrow in on one specific item: the DH in the NL, and specifically, the impact to pitchers. I’m comparing Rudy’s Steamer/Razzball projections from March to those here in July; we’ll focus in on changes in projected ERA.

At first glance, it’s easy to minimize this change. After all, we’re talking about 2-3 plate appearances per start, and pitchers aren’t complete zeros at the plate. In a reduced season, this is likely only 25-35 plate appearances over 10-12 starts. How big of a deal is it?

To set a baseline, let’s first look at the impact on AL starters. Here’s the top 50, comparing their March to July ERAs:

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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.

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