# NBA Night 2 Recap

I have always been a pretty casual NBA fan, but this year I am planning on taking NBA DFS a little more seriously. I am going to feel my way through these blogs and, similarly to how I approached baseball, see what works and what doesn’t. As always, I am focused on tournaments because I love chasing those monstrous wins.

## Daily Fantasy Basketball Night 2 Recap

Last night was my first NBA slate of the season and much like most NBA slates I’ve ever played, it was pretty damn mediocre. I am tired of that bullshit. Throughout the baseball season I adopted different strategies for selecting players and stacks and I tracked how each strategy worked out for me. I am going to be adopting the same strategy for this basketball season: Pick a strategy or theory, track it for a few weeks and see what is legit and what is just bullshit.

Since value in basketball DFS is so dependent on late-breaking injury news, I’ll start by taking a look back at the previous night’s slate rather than trying to make picks for the upcoming slates. Let’s go.

### How Much Do Vegas Totals Matter?

I was asking myself this last night while listening to a few podcasts before the slate. Naturally, Vegas totals seem like a good place to start. They give you a sense of expected tempo, offensive efficiency, and defensive strategy, but do they really end up deciding the slate?

I’m going to start with a simple system to track this:

1. Calculate the weighted total for each game on a given night compared to the other totals. For example, if the highest total on the slate was 230, that game’s weighted total would be 100, while a game with a 210 total would be 210/230 = 92. This will normalize every slate, so that nights when the highest total is 200 can be compared to nights when the highest total is 230.
2. Look at the lineup that won a large GPP the night before and see how many players from each game were in that lineup.
3. If multiple players were selected from the same game, record the number of players in that game next to its respective weighted total.
4. Track the frequency at which winning lineups contained stacks from different weighted total ranges (eg.: “25% of winning lineups have 4-man stacks from games with weighted totals between 90 and 92”).

Looking at last night’s winning lineup, I will input this data into my spreadsheet:

 Game Total Wtd. Total No. Players DET/IND 211 90.75 4 MIN/BKN 229.5 98.71 2

Over time, we should expect to see winning lineups have more stacks from games with higher totals than games with lower totals. If that doesn’t pan out, we can hope to see some sort of pattern developing over time and at least learn something from this fairly simple daily exercise.

### How Much Do Injuries Matter?

It is no secret that injuries to high-profile players are a tremendous source of DFS value, and it makes complete sense. If a player who plays 35 minutes and takes 20 shots every night is no longer playing, there are 35 additional minutes of floor time and 20 additional shots to be distributed between players who wouldn’t usually get that opportunity.

DFS sites have gotten better at adjusting pricing to account for these injuries if a player misses extended time, but there is still inevitable value here when pricing is released prior to an injury. My question is how much this value can really help you. I’ll take a similar approach here as I did for totals:

1. Determine which players missed time in games where DFS sites DID NOT have a chance to adjust pricing. This could be due to rest or injuries. The key is to find the games where the prices are based on these players being healthy.
2. Calculate the total salaries of every key player who missed the game due to injury or rest (salary should be a decent way to quantify both usage and minutes).
3. Look at the lineup that won a large GPP the night before and see how many players from each team were in that lineup.
4. Record the number of players from that team next to its respective Total Injured Salary (TIS).
5. Track the frequency at which winning lineups contained different quantities of players from different injured salary ranges (eg.: “25% of winning lineups have 2-man stacks from teams with \$8,000 worth of injuries”).

Looking at last night’s winning lineup, Blake Griffin ’s injury was really the most influential injury on the slate (I don’t know his price because it’s so early in the season, so I’ll use \$8,500 as an estimate):

 Team TIS No. Players DET 8500 4

There is no doubt that injury news provides value, but I am trying to determine how often you need these value guys to win GPPs. Over time, we can expect to see that the higher the salary of the player(s) missing the game, the more players you’ll want from that team.

I’ll be giving this a little more thought over the first few weeks of the season. I have to somehow account for nights with multiple key injuries (if Luka Doncic missed last night’s game then there would naturally be more value in Dallas and the Pistons would be less valuable), as well as times when injury value doesn’t actually mean GPP success (if Blake’s replacements busted last night). Once we have a larger data set, I am ultimately looking to be able to confidently make this type of statement:

Given that Player A is missing tonight’s game with a salary of \$X,XXX, you should play at least (#) players from his team.

### Player Narratives

Narratives or storylines surrounding players get a lot more attention in DFS than you might expect. You’ll often hear about a quarterback going up against his old team or a pitcher who’s pitching on his birthday. The general thinking is that certain off-the-field circumstances will cause a player to perform better (or worse) than expected.

This line of thinking can be pretty misleading, but can also pan out and give you a massive edge. For example, if a MLB hitter is going against his former team, he may want to have a huge game, but that doesn’t mean he will. Regardless of how much a baseball player wants to do well, they will be limited to their 3-5 at bats and honestly that extra motivation really shouldn’t improve the results.

Basketball is a much different story when it comes to narratives. If a player, coach, or team has extra motivation or wants to see a certain player have a big game, they can make that happen. We saw a prime example of this last night when Kyrie Irving had 50 points on 33 shots in his Nets debut. They were in front of their home crowd and wanted to give Kyrie a nice introduction to his newest fanbase, so they gave him unlimited opportunity to go off. And he did.

Rather than trying to quantify this, I’m simply going to keep a list of narratives throughout the season that work and don’t work. Maybe I’ll notice a trend, maybe each individual narrative will have to be evaluated independently. I’ll look for things like revenge games, birthdays, players with something to prove, and pretty much anything else the media will throw my way.

Here’s the start to what will likely be a completely useless list:

 Date Player Narrative PT/\$ 10/23/19 Kyrie Irving Home Debut 8.54

### Summary

This feels like a pretty good start to a new series of stat tracking. I’ll continue to grow this list over the course of the year to keep learning what works and what doesn’t. Comment below if there’s anything else you’d like to see me track. LFG.

## About the Author

• ### Matt Shanahan (mshanahan)

• Matt Shanahan is an engineer by trade and firmly believes in data-driven analytics. He grew up in Pennsylvania and graduated from the University of Pittsburgh. His betting strategy is to develop theories, create models to test those theories, and then collect data to see what works and what doesn’t.