# NBA Player Projections: Foundational Knowledge

Learning the baseline elements of NBA DFS is difficult enough, and that is true for players of all skill levels. When it comes to advanced stats and metrics, there is enough noise out there to make the average player’s head spin.

The most important thing to keep in mind to separate the signal from the noise: The majority of important factors are actually already considered in player projections. Projections are often misunderstood, and this article is designed for you to get a better grasp on how to use them.

**The Inspiration For This Article**

You’ll hear references to NBA advanced stats such as team pace, usage rates, and others every single day across the industry. The problem is that they are always referenced individually and rarely discussed in the context of a player projection. You almost never get a full explanation of why or specifically how much a player projection is affected by the information being discussed. This leads to stats being taken out of context, and decisions being made based on information that is either **already baked into a median projection** or is **better served as a reference to its effects on floor/ceiling**. We will now help you understand those two important concepts and the underlying advanced statistics that shape them.

### **What is a Player Projection?**

A player projection is attempting to deliver a prediction of the *most likely median outcome* for a player. Imagine you could play this slate 10,000 times. The median would be the point in which half of the outcomes were below this level, and half of them were above.

There are many methods – both simple and advanced – to estimate this value. Most models rely on accurately predicting two things when you break it down to the simplest level: 1) Playing Time (minutes) 2) Efficiency (points per minute).

The former is fairly easy to determine, but the latter is where math comes into play. We’ll talk about one way to start breaking that down below.

### **What is “Range of Outcomes”?**

Nothing is a simple as saying “we know this is going to happen.” Factually speaking, we don’t. Rather than think about players and projections in absolute terms, DFS should always be thought about as a range of possible outcomes. We mentioned above that we project the median, but perhaps more important than the median is understanding that every player has his own unique “range” of possibilities.

Here are some common terms to be aware of with regards to discussing range of outcomes:

**Floor:** Depending on whom you ask, it’s somewhere between the 10th and 20th percentile of outcomes for a player. That means of all possible outcomes, only 20% of them would be worse than the floor.

**Ceiling:** Depending on whom you ask, it’s somewhere between the 80th and 90th percentile of outcomes for a player. That means of all possible outcomes, only 20% of them would be higher than the ceiling.

When we project a player, we don’t just project his median performance. We also want to understand what his volatility level is and adjust our decisions accordingly.

**Understanding Player Projections**

Let’s walk through the process of projections at a high level and talk about where they come from. Like I said before, there are many ways to calculate a projection. I’ll speak about a top down approach in which we projected the team level performance, and use “rate stats” to distribute player efficiency accordingly.

**NBA ADVANCED STAT CONCEPT TO KNOW #1:** Team pace and “Projected Pace”

**WHERE CAN I FIND IT?** NBA First Look (Noto has his projected pace posted), RotoGrinders Team Stats

**WHAT TO KNOW:** Pace has several calculations and definitions, but let’s just call it a description of a team’s number of possessions per game. When you combine a team’s pace with the pace of the opponent (and compare to league average), you can then calculate **projected pace.** Projected pace is the basis of a player’s opportunity for the remainder of the projection process, and is a critical element to understand when analyzing a player for the night.

**WHAT NOT TO DO:** Don’t double count this in your analysis of the slate. RotoGrinders projections are attempting to account for pace already, so your only job here is to determine if there is any reason the game will go over or under the median expectation of pace. I’ll talk about “range of outcomes” and “floor/ceiling” a lot in this article. Determining your own thoughts on projected pace is one way you can start to predict if a player’s performance tonight will be closer to his “floor” or his “ceiling.”

Great. So now you know that pace=possessions and that number of possessions will dictate how we divvy up the results of each and every minute of the game. This raises a good question: How in the world do you predict the outcome of every possession in a game with any accuracy whatsoever? You’ll find the answer below.

**NBA ADVANCED STAT CONCEPT TO KNOW #2:** Player Performance Baselines and Rate Stats

**WHERE CAN I FIND IT?** Player baselines are abstract and projected, so they aren’t really published and are the basis of skill when it comes to making quality projections. You can however find the associated rate stats (listed below) in CourtIQ, on NBA.Com Advanced Stats , or on Basketball Reference.

**WHAT TO KNOW:** You can break down the results of every possession with the help of advanced rate stats. The first step in doing so is to set the **baseline** skill of each player from a rate stat standpoint. You could define **“baseline”** in several ways, but I will define it as **the most likely rate of production for each stat category in a neutral environment.** Once you have a baseline for every stat category, you would proceed from there to determine how the current matchup will affect each of the rates. Here is a list of 6 common advanced rate stats and their definition as listed on Basketball-Reference.com:

— **USG%:** Usage Percentage. An estimate of the percentage of team plays “used” by a player while he was on the floor. There are three ways that a possession can end (really, two). The first way is that a player can take a shot. The second is that a player can turn the ball over. The third is via a trip to the free throw line (which is basically a shot). If you are the player that performed any of those actions on a possession, you “used” that possession.

— **AST%:** Assist Percentage. An estimate of the percentage of teammate field goals a player assisted while he was on the floor.

— **REB% or TRB%:** Rebound Percentage. An estimate of the percentage of available rebounds a player grabbed while he was on the floor.

— **TOV%:** Turnover Percentage. An estimate of turnovers committed per 100 possessions.

— **STL%:** Steal Percentage. An estimate of the percentage of opponent possessions that end with a steal by the player while he was on the floor.

— **BLK%:** Block Percentage. An estimate of the percentage of opponent two-point field goal attempts blocked by the player while he was on the floor.

— Others you may want to know include 3PAR (3 point attempt rate), shooting efficiency stats (FG%, 3PFG%, FT%), and FTAR (Free Throw Attempt Rate).

All of these projected **rate stats** are applied to a player’s projected time on the court to determine how many possessions he’ll use, how many shots he’ll take, how many rebound chances he’ll see, and so forth.

**WHAT NOT TO DO:** Once again, don’t make the mistake of double counting. Projections try like hell to account for a lot of the stuff you are busy researching related to players’ usage and peripheral stats. What you want to be doing with the information you find on matchups, usage, and data you collect from on/off tools like CourtIQ is establishing a **range of outcomes**. We’ll talk about that more shortly.

Great. So we’ve walked through where projections come from, and it’s not some kind of simple multiplication problem. It all works together. One player’s missed shot becomes another player’s rebounding chance. Every player’s usage affects another player’s opportunity. You can’t have more than 100% usage. **Projections will distribute that usage/other rate stats accordingly with plenty of research behind them, and your job as a DFS player is to research the reasons (if any) a player will either exceed or fall short of his most likely outcome.**

## **Range of Outcomes**

Here is a quick recap of what we should have taken away from the above:

1) Projected pace determines total opportunity

2) Opportunity funnels down through median projected minutes and adjusted baseline stats to create a player’s projected median output.

Since we know projection models in almost all cases rely on some human element, that does leave them open to interpretation. Our projectionist could be “wrong” about a player’s median minutes expectation. He or she could be “wrong” about his current baseline production or the baseline adjustment based on matchup. This is where **thinking in probabilities** and **range of outcomes** becomes very important to evaluating projected player performance.

**NBA ADVANCED STAT CONCEPT TO KNOW #3:** Range of Outcomes and Floor/Ceiling Combinations

**WHERE CAN I FIND IT?** Here are some tools you can use to help determine outcome ranges. Ceiling and Consistency Tool , CourtIQ , Projected Stats Floor/Ceiling Projections (also on LineupHQ).

**WHAT TO KNOW:** Projections are not meant to tell you exactly how many points a guy will score tonight. They are meant to help you understand his most likely output. See the image below to help visualize this. This is a **normal distribution**, and a player projection is attempting to deliver you a number somewhere near the middle (not always the exact middle, as that is the “mean” whereas the “median” may be slightly off center).

So you can see that we are delivering just one of MANY possible outcomes for the night as his projection. Your daily research process should be focused on determining where within this outcome curve you predict the player will actually fall on tonight’s slate when it is all said and done.

To bring it all full circle, this is where we can start to take all those stats and data points people reference on a daily basis to make a call. Our point of reference is the projection, and now we must use our knowledge of the NBA to predict if a player’s performance will be closer to the floor or the ceiling. We also need to determine how wide that range is if we are playing in cash games (and to a lesser degree in GPPs too, though we care more so about ceilings in that format). Some tips for analyzing a player’s range of outcomes:

— **Minutes Volatility:** This is the easiest path to victory. Minutes equal money, and more precisely correctly predicting more (or less) minutes for a player than his median expectation can REALLY equal money. Some factors that can create variance in minutes include coaching tendency, matchup, foul trouble, injury, and in-game performance. If you can identify a plausible opportunity for a change in minutes, it can be especially valuable for lower-priced players in GPP formats.

— **Efficiency:** Baseline expectations assume a standard level of efficiency from a player. If you think you see a reason that a player could be more/less efficient than usual, then it is time to take a stand on your research! Maybe you think a matchup could lead to scoring efficiency or increased rates of defensive stats. Maybe a CourtIQ query suggests an increase in usage that you do not feel is reflected in the current projection. Whatever the reason, you should be looking at potential reasons for a player’s efficiency to change and where it might put him within his range of outcomes.

— **Pace Volatility:** Another way that a player could get more opportunity is via an increase in pace. Perhaps you think an injury will substantially increase or decrease the pace of play. Maybe the matchup itself has room for increased pace beyond the year to date numbers. The bottom line is that not all minutes are created equal, and that games with more possessions make every minute a player gets more valuable. Seek an edge on your opponents by identifying spots where pace could be different than expected.

It goes without saying that this is part of the skill in DFS, and that you may or may not suck at actually determining where a player might fall within his range. Don’t get bent out of shape- it’s REALLY hard to be good at this. No matter how good you are, focusing on the idea that a player has a whole range of outcomes (instead of just one that you stubbornly decide is definitely happening) can help you advance your game to the next level.

**WHAT NOT TO DO:** It is okay to be data-driven and focused on median projections in cash games. However, do not become a slave to projections in tournaments. Think in probabilities, and consider the full range of outcomes. Be aware of a player’s floor/ceiling combination. Be aware of situations that can increase a player’s potential to reach his ceiling. Do not make a habit of taking this concept to extremes, and remember that projecting range of outcomes is meaningless if you are consistently incorrect about where a player will fall within the range. In other words, it’s okay to speculate, but I suggest you do so with plenty of research backing your position and with an appropriate level of caution. A lot of work goes into player projections industry wide, and the NBA is the one sport where they matter as much or more than any other.

*Image Credit: USA Today Sports Images*

Learning the baseline elements of NBA DFS is difficult enough, and that is true for players of all skill levels. When it comes to advanced stats and metrics, there is enough noise out there to make the average user’s head spin.

There is a lot to know and consider on a daily basis, and I forgive anyone who struggles to fully understand the terms and stats that get thrown around liberally on a daily basis. The most important thing to keep in mind to separate the signal from the noise: The majority of these stats (or anything that matters) are actually already considered in player projections. Projections are often misunderstood, and this course is designed for you to get a better grasp on how to use them.

By the end of this course, you should be better prepared to understand what a player projection means, what goes into it, and how to make decisions based on a player’s full range of outcomes for the slate.

**The Inspiration For This Article**

You’ll hear references to NBA advanced stats such as team pace, usage rates, and others every single day across the industry. The problem is that they are always referenced individually and rarely discussed in the context of a player projection. You almost never get the full explanation of why or specifically how much that player projection is affected by the information being discussed. This leads to stats being taken out of context, and decisions being made based on information that is either **already baked into a median projection** or is **better served as a reference to its effects on floor/ceiling**. I will now help you understand those two important concepts and the underlying advanced statistics that shape them.