This edition of the Friday Dose skips a typical rundown of last night’s three-game slate, which saw the Bulls crush the Rockets in Chicago, the Bucks lose on the road to the Hawks, and the Thunder cruise to a 131-102 win the visiting Lakers. Rotoworld’s Mike Gallagher (@MikeSGallagher) did a great job breaking down those contests in last night’s player news blurbs.
What you will find below is a guide to making informed lineup decisions in points-based leagues during the fantasy playoffs, along with category-specific stats to aid head-to-head and roto owners. I am as usual providing free and unfettered access to my spreadsheets, which I’ve posted on Google Docs, and this week’s data-mining is particularly intense.
If you’re willing to dig through the stats there is no lack of interesting information available, including standard deviations, z-scores and more. For the sake of convenience and clarity I will summarize and explain four key findings: 1) how much fantasy value each team yielded to opponents over their past 10 games, 2) how fantasy values differ by positions and categories, 3) how much individual categories contribute to overall points-league values, and 4) an attempt to quantify expected points-league values for Friday’s 10-game schedule.
*Note: All of these statistics refer to the past 10 games for a given player or team. All of it was gleaned from NBA.com, Basketball-Reference.com, HoopsStats.com or DougStats.com, then formatted and (extensively) manipulated by the author.
First up is the team-by-team breakdown of fantasy points allowed to opponents. This should be familiar to anyone who read my column, Points League Primer in early January. That column explored multiple fantasy points-league scoring systems, but today I will focus on just one familiar system used by FanDuel and other sites, which awards the following points:
Made 3-pointer = 3
Made 2-point FG = 2
Made FT = 1
Rebound = 1.2
Assist = 1.5
Steal = 2
Block = 2
Turnover = -1
If you want to tailor this analysis to a specific points-based league, send me a Direct Message on Twitter @Knaus_RW and I’ll email you the full Excel file, in which you can tweak the formulas as much and as often as you desire.
|TOTAL Fantasy PTS Allowed||Team||Percentage of the mean|
|232.14||Los Angeles Lakers||117.65%|
|203.8||New Orleans Pelicans||103.29%|
|198.96||Oklahoma City Thunder||100.84%|
|195.14||New York Knicks||98.90%|
|192.78||Portland Trail Blazers||97.70%|
|192.62||San Antonio Spurs||97.62%|
|192.33||Golden State Warriors||97.47%|
|189.33||Los Angeles Clippers||95.95%|
It’s worth mentioning that in the full spreadsheet you can zero in on specific categories and filter the columns as you see fit. This is especially important for head-to-head owners. For example, no teams have been blocked more often over the past 10 games than the Nuggets, Rockets, Cavs, 76ers and Pelicans.
This brings me to my second key finding, a table showing how much each category contributes to fantasy values in a given position (PG, SG, SF, PF or C). You’ll notice that block-allowed accounts for very little in this points-based system, which devalues the category enormously when compared to head-to-head or roto leagues.
The results (based upon each team’s past 10 games) are strikingly similar to when I ran a similar analysis during the first week of January, so the relatively small sample size doesn’t seem to have had a significant impact. I also cross-checked the numbers using positional stats from HoopsStats.com and team stats from NBA.com, and the discrepancies were inconsequential.
In other words, 50.5 percent of a typical PGs fantasy value came from the ‘Points’ category, while 33.2 percent came from ‘Assists’. It’s no surprise that PGs rely more heavily on assists, and that centers easily rely the most upon blocks, but this quantifies what might otherwise be a vague strategy – in PG matchups we should absolutely target the Lakers, 76ers, Pistons, Cavs and Hawks, while avoiding the Nets, Grizzlies, Bobcats, Bulls and Blazers. Other quantifiable givens are that shooting guards are hugely reliant upon scoring, while centers can get away with limited offensive contributions so long as their opponent is weak on the glass.
The final line in the table above shows the percentage of all ‘fantasy points’ contributed by a given category. So for instance, ‘Points’ accounted for 53.0 percent of all fantasy points in this scoring format, ‘Rebounds’ were second at 25.2 percent, ‘Assists’ third at 17.3 percent, and so on.
I didn’t run the numbers for other scoring formats, including the defaults for Yahoo! and ESPN, but the numbers above were all within one percentage point of what I found in early January, so I feel confident submitting my former findings as a guideline to category values:
Rebounds = 34.0 percent
Points = 26.3 percent
Assists = 22.8 percent
Steals = 12.4 percent
3-pointers = 12.1 percent
Blocks = 7.8 percent
Turnovers = -15.3 percent
Points = 38.1 percent
Rebounds = 20.6 percent
Assists = 17.5 percent
FGA vs. FGM = 12.9 percent
Steals = 11.2 percent
3-pointers = 2.9 percent
Blocks = 6.6 percent
FTA vs. FTM = 2.1 percent
Turnovers = -11.8 percent
Remember that this is a league-wide guide to values…if you own Dwight Howard that 2.1 percent for FTA vs. FTM would skyrocket in importance.
Here’s where things get really interesting.
I also computed the points-league values for every player in the NBA over their past 10 games, according to the baseline scoring system I’ve been using throughout this column. Using a ‘percentage-of-the-mean’ calculation I had done earlier for each team’s value to opposing teams, I weighted every player’s 10-day fantasy averages by the (fantasy) strength of the team they are playing on Friday.
You can find the player-specific, matchup-weighted values for Friday’s games here.
For example, Paul George has averaged 35.5 fantasy points over his past 10 games. His opponent, Philadelphia, has yielded 114.3 percent of the league average to opponents during the same span, so George projects to score 40.6 fantasy points by this rough metric.
I say ‘rough metric’ because this misses a number of key factors, including the very category/position data I provided above. I’ve covered a lot of ground already and don’t have the time or space to sufficiently flesh out all of the implications, but maybe next season I’ll be able to combine these data cuts into one reliable and comprehensive system.
I’ll conclude with a failed attempt to list each player’s projected dollar-value in daily fantasy sports leagues like FanDuel, assuming a $60,000 salary. I did not include players who are projected to score under 15 fantasy points, as that seemed too scrubby even for a $3,500 player in FanDuel. I tried to quantify how much money each fantasy point is worth given a $60k budget (correcting for what I dubbed the ‘scrub differential’, since many players’ projected values fall below the $3,500 threshold), and you can see what I arrived at in this spreadsheet.
It has many flaws, some of which I’m keenly aware of, some of which I’ve not yet figured out. Ultimately, if you’re willing to play around with the numbers you should find it to be a useful guide.
That ends this descent into the statistical rabbit-hole. I’ve updated my rest-of-season schedule grid, so check that out if you’re still grinding away in the fantasy playoffs. As a final reminder… if you follow me on Twitter @Knaus_RW and send me a Direct Message, I’ll email you copies of these spreadsheets for you to use/copy the formulas and whatever else you please.