Aramis Ramirez, you’re not helping.

“So what do you do?”

 That’s the primary question I’ve been asked since last week’s rant against using batter versus pitcher data in DFS analysis. To reiterate, my beef isn’t with the stance on either side but rather with the defense of the respective stances. But let’s move on.

Today I’ll outline what I do. In subsequent discussions we’ll focus on each individual component with an explanation why I trust incorporating each into my little black box.

The process in question is how I generate a daily projection for each hitter and pitcher. Once the projection is generated, the anticipated number of fantasy points can be calculated and compared to the player’s salary to find those players expected to provide the best bang-for-the-buck.

HITTERS

The process begins with a rest-of-season project that is continually updated throughout the season to reflect any baseline skill changes gleaned from production to date. Since the projection incorporates the influence of the hitter’s home venue, this impact is stripped out to render a park-neutral projection.

Each pertinent stat (those used in DFS scoring) is normalized to per plate appearance. And now the fun begins!

Sequentially, each of the following are used to adjust the park neutral projection

  • Home/Away
  • Handedness
  • Park factor
  • Quality of opposing starting pitcher and bullpen

Next, an adjusted per plate appearance projection for all the apropos statistics is determined. Each MLB team has an expected number of plate appearances for each lineup spot which can be adjusted based on the expected scoring that day. The projected plate appearance is multiplied by the per plate appearance projections to yield the actual number of each stat the player is projected to garner that day.

The first run each day uses the number of plate appearances each player is expected to receive based on recent position in the lineup versus left and right handed pitchers. Once the lineups are announced, the actual lineup spot is used to fine tune the bang-for-the-buck computation.

PITCHERS

The process for pitchers is fundamentally the same as that for hitters but instead of a player on player adjustment, the opposing team’s numbers versus a pitcher of that handedness are used. The rest is essentially identical with the exception that the win projection is tied into, but not completely dependent on the Vegas odds.

USING INDEX FOR ADJUSTMENTS

At the crux of each adjustment is using what I’ll call an index. By means of example, the present league average K/9 is 7.6. Let’s take a pitcher with a projected K/9 of 8.5. The K/9 Index is computed as follows:

8.5/7.6 = 1.12

Now let’s take a hitter projected to strike out 0.5 times in that game. The adjusted strikeout projection is:

0.5 x 1.12 = 0.56.

Please realize this does not consider the handedness of the match-up. This is an example of quality of opponent. The handedness is dealt with independently.

As will be demonstrated as we journey through all the adjustments, they’re all a series of index calculations, handled seamlessly with Excel. All the little black box needs is the names of the players, the park, whether the game is home or away and the lineup spot. This information triggers the appropriate adjustment with periodic updating of all the league and player average baselines.

Hopefully this gives you a broad based idea of what I do to generate the projected points per day for each player. As always, I’m happy to address questions pertaining to this discussion in the comments.