With our Trends tool, you can see current and historical matches for players in matchups that meet the specified criteria. This makes it pretty easy to track performance within the result set. In this series, I thought it might be cool to take it one step further. I will be creating a Trend early in the week, playing the “Current Matches” in my lineups throughout the week on FanDuel and then reviewing the Trend at the end of the week.
This Monday, I created the following Trend:
Description
I think a lot of DFS players like targeting point guards in games with high projected point totals. This week, I decided I’m going to create a trend for this and take it for a test ride. I did add a salary filter so that only starting point guards would be included in the results. I also added a spread filter to exclude games with a high blowout potential – the idea is to look at point guards who are going to be involved in close, high-scoring games.
As you can see, the results are pretty positive overall:
The filters I used were:
- Player’s average salary is between $6,000 and $13,000
- Player’s position is PG
- Closing total is between 205-225
- The spread is between -6 and 6
Results
The screenshots below are from teams I entered into FanDuel’s $5 Layup
12/14
What a difference a year makes – two of the most highly owned options on 12/14 were John Wall (23.9%) on the road against Memphis and Jameer Nelson (47.6%). I don’t think there were too many nights in 2014-2015 where you could have said that. I did have a bunch of Jameer elsewhere, but he was not identified by this trend because his salary was not above $6,000 – same thing with Patrick Beverley on the other side of the game. I’ll address this a little bit more in the conclusion.
Anyway, three of the four teams identified by the trend went over 100 points, with the exception being Phoenix. Although the Suns only lost by 11 points, they were actually down by 22 points heading into the fourth quarter, making this game an undercover blowout. Sometimes, you can take steps to avoid blowouts and it happens anyway – just part of the game. As a result of the blowout, both Knight and Williams failed to meet their implied point totals.
In the NOP-POR game, both point guards exceeded their projection – Tyreke was close to 20 points above his 28.83 implied point projection. This was probably pretty close to a perfect example for this trend – both teams have been poor against PGs and the game was expected to be close and high scoring. I was pretty surprised to see Tyreke’s ownership at just about half of what Lillard’s was even though he had a very reasonable $7,300 salary.
The top play was probably Reggie Jackson (9.2%, 55.7 points). The Pistons game against the Clippers had a 204.5 over/under, so with just a minor tweak, he would have appeared in the results for this trend. One thing that can be challenging when creating a trend is figuring out where exactly you should draw the line.
12/15
This was a four-game slate and there were no matches within the trend. If I were to change the salary filter to a projected minutes filter, the results would have included Darren Collision and Patrick Beverley (HOU @ SAC, O/U 221.5, SAC -2.5). I would highly recommend making this change and setting the projected minutes filter around 28-30. The last thing you want to do is exclude the best value plays on the board.
12/16
MIN @ NYK was not on my radar for this trend at all due to an O/U of just 198.5, but it’s pretty cool when a guy named “ricky_rubio” wins a GPP due to a near quadruple-double from…Ricky Rubio.
Anyway, the match tonight was Deron Williams. The Pacers-Mavericks game was projected to be the third-highest scoring game on this 12-game slate, behind GSW-PHX and OKC-POR, which were both projected to be blowouts.
The 1.8% ownership on a guy who had been exceeding expectations recently was nice, but unfortunately, this game was an unmitigated disaster for the Mavericks. That’s now twice in three nights where Vegas projected the Mavericks to be in a close game and they were completely wrong. When you use this trend and the game gets out of hand, unfortunately, the results are not going to be pretty.
Conclusion
The good thing about testing a trend is that it’s pretty easy to figure out how you can improve on it by comparing the trend matches to the top plays that night. In this case, using projected minutes instead of salary would have been a major improvement.
When I made that adjustment to this trend, the Plus/Minus raised by over a full point:
New Trend Filters:
- Player’s position is PG
- Closing total is between 205 and 223
- Spread is between -5 and 5
- Player’s projected minutes is between 28 and 44
If I had used this trend for the full week, 9-of-13 plays would have exceeded value (the only new result to miss value would have been George Hill in the blowout DAL-IND game). These are the filters I’d recommend you setup if you do decide to copy this trend for your own use.
