Alright, guys, we’re going back to school today to go over the basics of evaluating whether a player has a good matchup on any given day.  I’ll let you copy my work as long as you promise not to give me a swirly, stuff me in a garbage can, or pull my gym shorts down in front of the cute girl I’m crushing on but don’t have the confidence to talk to.  Man, college was a rough chapter of my life…

Today we’ll do hitters and then do pitchers next time out.  Also, don't get overwhelmed half-way through with the sheer number of factors and my ostentatious "evaluating-this-factor-correctly-takes-hard-work" attitude.  I've included a simple formula for evaluating hitters at the end that should serve as a great starting point for those that are completely new to this or don't have the time to figure out which catcher is best at scaring would-be-basestealers into not running.

True Talent Level — This one may seem obvious, but it’s the most important factor when figuring out a player’s value (after the even more obvious “make sure he’s in the lineup today” factor).  It sets the baseline expectation, which we then layer a slew of contextual factors over on any given day.  It should also be noted that it’s important not to confuse True Talent Level with a player’s actual performance.  As I’ve shown before, a player’s salary is heavily influenced by his current season performance (more heavily on some sites than others), but a player’s actual performance can be far from a reflection of his true talent level.  There’s big value to be found just by identifying players who are underperforming.

Opposing Pitcher — The quality of the opposing pitcher is perhaps the most important contextual factor to take into consideration when you’re setting your daily lineup.  The worse the pitcher, obviously, the better.  Also consider the pitcher’s fly ball/ground ball tendencies.  Fly ball pitchers make for better matchups against power hitters, and ground ball pitchers are relatively better matchups for hitters that derive their value elsewhere (average, speed, etc.)

Opposing Defense — Depending on how you’re evaluating the pitcher, this may be built in already, but obviously it matters whether you have Mike Trout in center field or the Pillsbury Dough Boy.  Additionally, defense is less important for players who rely on home runs and walks for their value and more important for contact hitters.

Platoon Effects — I’ve explored platoon effects a bit before, but their importance can’t be understated.  Righties hit better against lefties and vice-versa.  One of the big pitfalls that daily fantasy players fall into is looking at individual player platoon skills for the current season or the past three years or some small time period.  Platoon effects take a long time to become reliable (more so for righties than lefties), so the rule of thumb is to always look at career data and only for established veterans with many years of experience.  For younger players, unless you really know what you’re doing, better to just assume their platoon splits are league average and abide by the “righties versus lefties” and “lefties versus righties” rule.  Also, while switch hitters will always have the platoon advantage, they may not be as good of plays as that line of logic suggests.

Home Field Advantage — Home field advantage is not an enormous influencer of player performance, but it is important. Suffice it to say, players on the road take a value hit.

Ballpark — Everyone knows that ballparks play differently, but most people just consider them in terms of home runs or hits.  Home run park effects are the most important and largest ones, but few realize that parks also affect things like strikeouts and walks.  Parks can also affect left-handed batters differently than right-handed batters.  A lot of daily players will simply go to ESPN and look at their single-year, overall park factors, but take heed: single-year park factors can be highly deceiving.  As a rule of thumb, you should be looking at a minimum of three years worth of data, as a starting point.  They don’t do them the same as I do mine, but StatCorner uses a pretty solid method and a number of years when calculating theirs, if you’re looking for a reference guide.  Another big pitfall some daily leaguers make is relying on “career in park” data.  If a player is a home run machine in PNC Park, chances are that’s not going to continue since PNC Park is extremely unfavorable to hitters.  He’s just gotten lucky.  Ignore these numbers and rely on the park factors instead.  Also, keep in mind that park factors are less important for big power hitters who blast their home runs well beyond even the deepest fences.

Weather — In line with the effects of ballparks is the effect of weather.  I’ve yet to do much extensive research into weather, but in general terms, wind blowing in is bad for power and wind blowing out is good for it (duh).  Temperature, humidity, and such also play a part, but I’ll be delving into weather more in the future.  And of course, weather needs to be taken into consideration because rain can lead to a game not being played.  You don’t want a player in such a game in your lineup.

Lineup Position — Most big name players stay in the same lineup spot throughout the year (Miguel Cabrera will always bat third with Prince Fielder behind him in the cleanup spot), but for the fringier guys, who will make up a big chunk of your lineup on any given day, it’s important to pay attention to where they’re batting.  Generally speaking, the higher up in the order, the better.  The first two spots are better for average, on-base, or speed guys while the 3-5 spots are better for power hitters.  There’s a big drop-off in expected value after the fifth spot, and anywhere from six through nine is unfavorable.  Pay particular attention when a player who normally bats in one spot is batting in a different one.

Umpire — Despite playing a part in the outcome of almost every single pitch, the umpire is rarely a consideration for daily fantasy players.  Part of this is a lack of easily accessible data.  Another part is not quite knowing what to do with this data.  Just looking at the raw numbers is a recipe for disaster.  I’ve developed a system for umpires that I’ll be talking about at some point in the future.  Umpire effects are limited to strikeouts and walks, so they are of a bit more use for evaluating pitchers, but those stats still count on most sites for hitters and have an indirect effect on batting average.  My results are still preliminary, but it seems that a good or bad umpire has about the same effect as a good or bad park does—and a greater effect for certain batter/pitcher handedness combinations.

Catcher — Catchers are important for two primary reasons: 1) They can have a large impact on ball/strike calls and thusly the strikeout/walk/contact outcome and 2) They have a huge impact on a player’s stolen base potential.  Great work has been done on the former by the likes of Mike Fast and, more recently, Max Marchi at Baseball Prospectus.  On the latter, data is available, although like with umpires, working with it can be a little tricky.  More on this in the future.

Hot/Cold Streaks — Lesser analysts love to use hot and cold streaks to prove their points or make their player recommends.  Others will piddle around by saying “well, the sample size is small, but this happened so I’m going to talk about it anyway.”  There’s no room for pussyfooting here.  Hot streaks should be ignored completely, and cold streaks should only be relied upon if they’re dragging on for an inordinate period of time (think B.J. Upton).  It’s far more likely that a player is having mechanical or psychological difficulties than that he’s swallowed a bottle full of Great Fairy’s Tears and is suddenly going to play like Babe Ruth for the next few games.  As I’ve discussed before, there’s a big difference between statistics and sabermetrics.  Any chump can look at data, but it takes an extra level of skill to decipher what the data means, whether it’s important, and how best to use it.  You can see some of my previous work on hot streaks and cold streaks here.

Career vs. Pitcher Data — In a similar vein, the way a hitter has performed against a given pitcher is mostly irrelevant.  Unless the hitter has faced the pitcher over 100 times or you have some sort of scouting data to support the smaller sample, you have to ignore it.  The advice of so-called experts that point to Player X’s career .500 average in 10 at-bats against Pitcher Y should be disregarded.  My previous work on batter vs. pitcher data can be found here.

A Simple Formula To Get Started

As you can see, there are a lot of factors that go into figuring just how favorable a hitter's matchup is on any given day.  I know that I didn't exactly provide explicit details on how to evaluate some of those factors.  For some, it's because I'm still studying them myself.  For others, it's because to do them right (or even at all) takes a lot of work, but that's why DFSEdge is here: to do most of that heavy lifting for you.  If you're a do-it-yourself kind of person, though, and want a simple formula for evaluating players yourself, pay particular attention to True Talent Level, Opposing Pitcher, Ballpark, Home Field Advantage, and Platoon Advantage.  It won't rival the guy who's got the big fancy algorithm that puts all of these factors together with 100% efficiency, but it will let you beat the guy that is going by his gut or stacking his favorite team or is relying on advice that is based on streaks and trends.