We truly believe here at FantasyLabs that we have the most unique tools and data available to DFS users. However, we also realize that those tools and data are only as awesome and helpful as our ability to effectively communicate how to use everything. As such, we will continually listen to feedback of what you need and try our best to teach our subscribers how to use all of the cool things we have to offer.
With this in mind, we’ve begun a “Trend of the Day” series. Every weekday we’ll walk our subscribers through an important trend for that day’s slate of games.
During the first couple months of the NBA season, I feel like the effect of players playing on back-to-back nights is a bit overstated. After the All-Star Break though, with a handful of teams effectively ruled out of the playoff picture, we need to be more cognizant of back-to-backs. That brings us to Carmelo Anthony tonight, who is currently the third-rated small forward on DraftKings in the Phan Model for his matchup with the Suns.
Trend: Carmelo Anthony vs. PHX
Actually, the Trend is not going to be specific to Melo. Instead, I’m going to look at expensive players on losing teams in back-to-back games over the final weeks of the regular season. The next time Giannis, Cousins, or Anthony Davis play on the second half of a back-to-back in 2015-2016, this Trend will be applicable. This makes for a cleaner headline though.
Step 1: Time Filters > B2B > Select “2H & 2A”
We start this Trend in the same place we’ve started several other Trends recently — by applying the back-to-back filter. When you look at all players playing for all teams in back-to-backs, the results are actually slightly positive.
Step 2: Player Filters > Salary > Set “$8,000 to $13,200”
Next, we filter out anyone whose price was lower than $8,000 on DraftKings. That cuts the Plus/Minus in half, but we’re still over zero.
Step 3: Team Filters > Team Win % > Set “0% to 45%”
Now we’re going to apply a “bad team” filter. You can play around with this a bit to figure out how exactly you want to define a bad team. Personally, I went with teams whose win percentage was under 45% coming into the game. That tanks our Plus/Minus and we are now pretty comfortable below zero:
Step 4: Time Filters > Game Number > “Set 65 to 82”
As I alluded to in the introduction, the main thing I’m interested in is performance towards the end of the regular season. Tonight will be Game Number 66 for the Knicks, so I’m drawing the line at 65-82 here. You could also use a month filter instead, but I prefer using Game Number. I really want to focus on the last 15-20 or so games where all hope is truly lost among the NBA’s worst teams.
We are starting to get pretty specific with our filtering, so the result set only includes 18 matches, but the results are pretty bad. Also, if you look at the players who have exceeded value in the subset, they’re young players who are in the midst of a breakout year:
In other words, not Carmelo Anthony.
This example demonstrates why it is important to use Trends and Player Models (Phan Model on DK) together. Anthony has a very high rating at the time of this writing in Player Models, and for good reason. He’s playing in a pace-up game against an inferior opposite and he commands a 90% Bargain Rating. If he gets his normal allotment of minutes and plays like he is capable of, sure, he will be a good play tonight. But this Trend gives us reason to doubt that will be the case.
