How I’m Spending My All-Star Break: An Exploration Into MLB Trends

Ahh, the All-Star Break, the time when those of us who focus on NBA DFS attempt to master a new sport. Maybe we’ll take a shot at college basketball or give NHL a try.

Not this guy.

Instead of my usual donation to the sharks of the previous respective sports this time of year, I’ve decided to forgo my usual charitable activities. Instead, I’m going to use my extra time in a way that will be more productive and, if nothing else, save my bankroll from it’s usual depletion while the NBA is on hiatus. This year, I’m going to start my MLB research early. And for a procrastinator such as myself, this is no small feat.

I haven’t spent a ton of time digging into our Trends tool for MLB, so this seemed like the perfect time to start and walk our readers through some of the functionality of the tools while I’m at it. My goal with all of this? Attempt to flush out a few trends that the best players from last April had in common and see if any of these trends are predictive of how players as a whole perform early in the season.

First, I’ll isolate the top-20 players from last April in terms of average DraftKings (DK) points per game, for players that appeared in at least 10 games.

Step 1: Time Filters > MLB Season > Select “2015”

Step 2: Time Filters > Game Month > Select “April”

And here’s our initial population and how they performed.

Now here’s where things get a little bit manual, as we select the top 20 performers.

Step 3: Player Filters > Player Name > Sort by Avg Pts > Select Top 20 Players w/ at least a Count of 10

You’ll have to go select the checkbox next to those you want to include — this may take a bit. But now let’s look at our finalized population…

From this point, I realized that I was really going to have to start digging to determine what many of these players had in common. After running through a multitude of filter changes, two trends really stuck out to me – the majority of these players played their games above 50 degrees (87.5%) and were priced between $4,000 and $5,500 (81.1%). Now that we’ve identified these, let’s test them out on the entire population — to do this we’ll remove the “Player Name” filter and the “MLB Season” filter.

Now that those filters are removed, we’re left with a population that consists of any player that has played in the month of April the past two seasons.

This will be our basis to test each trend on. With that set, let’s move on to the first filter.

Step 4: Player Filters > Salary > Set to “4000 to 5500”

Not a significant increase, but a nice little bump in Plus/Minus – for those coming over from other sports, the Plus/Minus probably seems miniscule, but consider that Bryce Harper’s Plus/Minus of +2.21 was one of the best last year. The scale simply won’t be as extreme as you may see in NFL and NBA.

Let’s remove that filter and test out the next one.

Step 5: Weather Details > Temperature > Set to “50 to 92”

This one was disappointing, as the results were almost identical to our control group. On the surface, the trend makes sense — players perform better in warmer temperatures. Just as I had written this one off as noise, I decided to change the filter slightly to increase our sample size a bit.

Step 6: Weather Details > Temperature > Set to “60 to 92”

Now that is more promising. Part of what I love about the Trends tool is the ability to tinker and adjust. The thought process behind the initial test was sound – players play better in more comfortable temperatures – but the provisions I had set with the original filter were too strong and we weren’t really filtering out many players.

Finally, let’s test out both filters on the population at the same time. Keeping the temperature filter in place, let’s add back in the price filter from earlier.

Step 7: Player Filters > Salary > Set to “4000 to 5500”

Not too bad. Not in the elite category of our original 20-player control group, but that’s to be expected. All in all, this isn’t a bad place to begin when starting the process of configuring trends for the upcoming season.

Conclusion

Let’s wrap up this extremely early exploration into MLB research with a few thoughts on the data we’ve uncovered.

The salary filters make a ton of sense to me. Earlier in the season in any sport, there will be a pricing correction that will occur after the first month or so. Playing a balanced lineup early on can often lead to a more stable group of players. Additionally, I don’t think many would be surprised to learn that players in colder games are apt to struggle a bit more.

If you find yourself struggling to fill the time over the next week, give the Trends tool a try and see if you can’t uncover some interesting trends of your own!