The purpose of this resource is to organize the different data sources and tools I use when researching NFL spreads, totals, and player props. There is no single metric or website that can fully explain a matchup. The goal is to combine quantitative analysis with an understanding of how teams actually play, such as personnel, formations, coverage structures, pressure tendencies, run schemes, and individual player roles to develop a more complete picture of a game.

A major part of the process is separating what happened from why it happened. Basic statistics and rankings can be useful starting points, but they often lack the context needed to determine whether past performance is predictive going forward. For example, knowing that a defense ranks near the bottom of the league in receiving yards allowed tells us there may be an area worth investigating. It does not tell us whether those yards are the result of coverage structure, poor tackling, explosive plays, game script, opponent quality, injuries, or simply facing a high number of pass attempts from defense constantly being on the field with the opponent consistently trailing.

For spreads and totals, I want to first establish how good each team actually is before breaking down how their strengths and weaknesses interact. Metrics such as EPA per play, success rate, DVOA, explosive-play rate, and early-down efficiency provide a starting point for measuring team performance. From there, the matchup can be evaluated through personnel usage, formations, run concepts, coverage tendencies, pressure rates, offensive and defensive line play, pace, injuries, and expected game script. The objective is not simply to determine which team is better, but whether the specific matchup creates an advantage that is not fully reflected in the current market.

Player props require a slightly different approach. Opportunity comes first. Before evaluating whether a matchup is favorable, I want to understand what role the player is expected to have. For receivers, that means looking at snap and route participation, target share, targets per route run, first-read share, alignment, air yards, and red-zone usage. For running backs, it includes snap share, carry share, route participation, target involvement, goal-line work, and the types of run concepts being called. Quarterback analysis requires evaluating expected passing volume along with pressure, blitz, coverage, and defensive structure.

Once the player’s expected opportunity is established, the next step is determining how that role matches up with the opponent. A receiver who performs well against zone coverage becomes more interesting when facing a defense that plays zone at a high rate, but the analysis should go further than the basic split. Where does he align? What routes does he run? What coverages does the defense use within its zone structure? Where are the voids in those coverages? Does the offense regularly call concepts designed to attack those areas? The same principle applies to rushing props: instead of simply looking at rushing yards allowed, the goal is to understand how a team’s preferred run concepts match up with the opponent’s fronts, box counts, personnel, and ability to defend those concepts.

The final piece is the market. A strong football matchup does not automatically make something a good bet. The available line and price determine whether the identified advantage still has value. Opening lines, current lines, alternate lines, movement across sportsbooks, and differences between sharper and more recreational markets all provide information about what has already been priced in.

The sources below are organized around these different parts of the research process. Some are primarily used for discovering potential bets, while others are better suited for measuring team strength, evaluating player usage, breaking down scheme and matchups, tracking injuries, or analyzing the betting market. The purpose is not to use every source for every bet, but to know which source provides the best information for the specific question being asked.

Research Framework

The research process can be broken into several layers. Each layer answers a different question, and the importance of each one will vary depending on whether I am evaluating a side, total, or player prop.

1. Market

What is the market currently pricing?

Before getting too deep into a matchup, I want to establish the current betting market. This includes the opening line, current line, price, alternate lines, and how those numbers differ across sportsbooks. Line movement can provide additional context, but movement itself is not a reason to make a bet. The purpose is to establish the number I am trying to beat and determine how much of an expected advantage may already be reflected in the price.

Key information:

• Opening line

• Current line

• Best available price

• Alternate lines

• Line movement

• Differences across sportsbooks

• Market consensus

2. Team Quality and Efficiency

How good are these teams independent of the matchup?

The next step is establishing a baseline for each team’s performance. Win-loss record and traditional per-game statistics can be misleading because they are heavily influenced by opponent quality, game script, turnovers, and other forms of variance.

Efficiency metrics provide a better starting point for evaluating how consistently an offense or defense is performing on a play-to-play basis.

Key information:

• EPA per play

• EPA per dropback

• EPA per rush

• Success rate

• DVOA

• Explosive-play rate

• Early-down efficiency

• Third-down efficiency

• Red-zone efficiency

• Strength of schedule

3. Tendencies and Scheme

How does each team actually play?

Overall efficiency establishes team quality, but it does not fully explain how that performance is being generated. The next step is identifying the structures and tendencies behind those results.

Offensively, this includes personnel usage, formations, pass rate, run concepts, motion, play action, and other tendencies. Defensively, the focus shifts toward coverage structure, fronts, box counts, pressure, blitz tendencies, and how the defense responds to different offensive personnel and formations.

Key information:

• Personnel usage

• Formation usage

• PROE

• Pace

• Motion

• Play action

• Run concepts

• Man vs. zone

• Coverage shells

• Single-high vs. two-high

• Blitz rate

• Pressure rate

• Defensive fronts

• Box counts

4. Player Opportunity and Role

Who is actually receiving the opportunities within the offense?

This becomes especially important for player props. Player production is ultimately dependent on opportunity, and raw per game averages can hide meaningful changes in a player’s role.

The goal is to determine both the player’s current involvement and whether that involvement is stable, increasing, or decreasing.

Key information:

Receivers

• Snap share

• Route participation

• Target share

• Targets per route run

• First-read share

• Air-yard share

• ADOT

• Alignment

• Red-zone usage

Running Backs

• Snap share

• Carry share

• Routes

• Target share

• Goal-line carries

• Red-zone opportunities

• Two-minute role

• Third-down role

Quarterbacks

• Dropbacks

• Designed pass attempts

• Scrambles

• Designed rushing attempts

• Red-zone rushing usage

5. Matchup

How do the strengths and tendencies of one side interact with the other?

This is where team and player data need to be connected rather than evaluated independently. A matchup is not favorable simply because an opponent ranks poorly against a position. The goal is to determine whether the offense’s preferred way of generating production attacks an area where the defense is vulnerable.

For the passing game, this can involve matching receiver alignment, route usage, and performance against specific coverages with the opponent’s defensive structure. For the running game, it can involve comparing offensive run concepts with defensive fronts, box counts, personnel, and efficiency against those concepts.

Examples:

• WR alignment vs. CB/coverage matchup

• Route usage vs. defensive vulnerability

• Receiver performance vs. man/zone

• Receiver performance vs. specific coverage shells

• QB performance vs. pressure

• QB performance vs. blitz

• Offensive line vs. defensive front

• Run concept vs. defensive structure

• Personnel efficiency vs. opponent personnel defense

6. Game Environment

What type of game should I expect?

Even a favorable individual matchup can fail to produce if the game does not create enough opportunities. Expected play volume, scoring environment, pace, weather, and likely game script all influence the number of opportunities available to both teams and individual players.

Key information:

• Spread

• Total

• Implied team total

• Expected pace

• Expected play volume

• PROE

• Game script

• Weather

• Stadium

• Injuries

7. Price and Decision

Is the edge large enough to justify a bet at the available number?

Finding an advantage within the football matchup and finding a good bet are not the same thing. The final step is returning to the market and determining whether the current line still provides value.

A player may have an excellent matchup at 65.5 receiving yards but no longer be attractive after the market moves to 78.5. Similarly, identifying the correct side of an NFL matchup does not automatically mean that side remains valuable at every spread.

The final decision should therefore consider both the strength of the underlying handicap and the price being offered.

Final question:

What am I projecting differently from the market, why do I believe the market is wrong, and is the difference large enough to bet?

Source Directory

The sources in this section serve different purposes within the research process. Some provide the underlying football data needed to evaluate teams and players, while others are better suited for identifying tendencies, analyzing scheme, tracking the betting market, or finding potential props to research.

There is significant overlap between several of these platforms. The goal is not to check every source for every matchup. Instead, each source should have a defined role based on the information it provides best.

McAnalytics

Primary Role: Player Prop Research, Matchup Analysis, and Team Efficiency

McAnalytics is one of the more versatile tools in the research process because it brings player production, usage, matchup context, and sportsbook pricing into the same interface.

Its biggest strength is the ability to move quickly from a player’s recent results into the underlying conditions that may have produced them. Game samples can be adjusted, current prop lines can be compared directly against recent production, and player performance can be filtered using variables such as snaps, routes, targets, air yards, carries, box counts, opponent tendencies, and teammate availability.

This makes McAnalytics especially useful for identifying whether a player’s recent production is being supported by a stable role or driven by circumstances that may not repeat.

Key Data and Tools:

• Adjustable player game logs and sample sizes

• Current prop lines and best available Over/Under prices

• Player averages and hit rates

• Snaps and routes run

• Targets and receiving air yards

• Deep-target opportunities

• Rushing attempts and rushing efficiency

• Defensive box-count filters

• Opponent coverage frequency and league rank

• Opponent passing, rushing, air-yard, and YAC metrics

• Situational performance filters

• Teammate active/inactive filters

• Advanced team passing, rushing, and receiving metrics

• Pressure, sack, scramble, and box-count data

The teammate filters are particularly valuable when injuries or depth chart changes alter an offense. Instead of relying on full season averages, a player’s game sample can be narrowed to situations in which a specific teammate was active or inactive, providing a quick way to investigate potential changes in targets, routes, carries, or overall usage.

The platform also provides useful team level context. Advanced passing, rushing, and receiving metrics can help identify whether production is coming through air yards, yards after the catch, yards before or after contact, pressure, stacked boxes, or other underlying factors rather than relying only on traditional per game statistics.

Best Used For:

• Initial player prop research

• Identifying changes in player opportunity

• Comparing production with current betting lines

• Teammate on/off analysis

• Injury-related role changes

• Opponent coverage tendencies

• Air-yard and YAC analysis

• Pressure and sack tendencies

• Defensive box-count analysis

• Advanced team passing, rushing, and receiving context

The Coachspeak Index

Primary Role: Injuries, Player Usage, Depth Charts, and Coaching Information

The Coachspeak Index tracks comments from NFL head coaches, offensive coordinators, and general managers and provides context for determining how much weight should be placed on what coaches say publicly.

Its primary value is not simply finding coach quotes, but evaluating those comments against each coach’s historical reliability. Coach statements are tracked across categories such as injuries, player usage and workload, depth charts, and transactions, helping distinguish potentially actionable information from standard coachspeak.

Key Information:

• Injury updates and expected timelines

• Player usage and workload comments

• Depth-chart information

• Potential role changes

• Backfield and receiver rotations

• Coaching and coordinator comments

• Personnel and transaction information

• Historical coach reliability

• Context from HC, OC, and GM press conferences

The Coachspeak Index is particularly useful when quantitative data cannot yet reflect a change. If a coach indicates that a running back will receive a larger workload, for example, there may be no recent-game sample showing that role yet. The Coachspeak Index can provide additional context for determining whether that statement should influence the projection.

It should not replace usage data once that information becomes available. Instead, it can help identify potential changes before or while they begin appearing in snaps, routes, targets, carries, and other underlying opportunity metrics.

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It was a 2-0 day. Both plays were in the Liberty vs Sky game. The first play was Kamilla Cardoso Over 2.5 Assists. She finished with 3 while basically not really playing the first quarter due to foul trouble. The second play was Breanna Stewart Under 3.5 Assists. She finished with just 1 assists. She didn’t play the 4th quarter but she wasn’t close to hitting this line either.

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Bad day yesterday going 0-2. Simply put just bad reads with Carla Leite Under 15.5 Points and Rhyne Howard Under 17.5 Points. Both had huge games here but Rhyne did it off big volume while Leite was pure efficiency.

Kamilla Cardoso Over 2.5 Assists (-112 Draftkings) 0.75u

Cardoso has had 4, 5 and 4 assists on 4, 8, and 7 potentials against the Liberty this season. These assists came off either doubles or handoffs last game and I am expecting something similar here. The Liberty are the 4th worst in assists vs her position this season and have allowed the 5th most apg the last 7 games. If we look at games without Diggins and while facing a bottom 5 assists defense, she’s over this line in 6/7 games and averaging 3.7 apg on 7 potentials per game.

Breanna Stewart Under 3.5 Assists (-118 Fanduel) 0.75u

Stewie has gone under this line in 2/3 games against the Sky this season and if we look at the last 7 games, the Sky rank 1st in frequency allowed on catch & shoot looks and 46% of her assists have come off those looks. The Sky overall are allowing the 4th lowest 3pt frequency and 57% of Stewies assists have been threes recently. This is a top 7 defense for assists vs her position. If we loo kat games with Ionescu and facing a top 7 defense vs her position, she’s under this line in 9/13 games while averaging 2.5 apg on 5.6 potentials. This is a much better spot for Ionescu to rack up assists as 68% of her assists come in the interior (last 7 games) and the Sky are allowing the 5th highest interior frequency.

It was an 0-2 day on Wednesday. The first play was Tiffany Hayes Over 11.5 Points. Only played about 3 minutes with 5 points. Your guess is as good as mine on why the minutes were cut. Of course on the second leg last night she went over this line. The last play was Laura Juskaite Over 1.5 Threes Made. She finished with just 1/3 shooting from deep. Expected a little more volume but the entire team was abysmal from three here, shooting tour dates of 4/26.

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Good day yesterday going 3-0. The first play was Carla Leite Over 6.5 Assists. She finished with 7. It was good to target threes against this defense but everyone struggled to shoot the three ball here which gave her a slow start to her assists. That fourth quarter run help a ton for this prop. The second play was Sonia Citron Over 4.5 Assists. She finished with a team high 9 assists here. The final play was Michaela Onyenwere Over 1.5 Threes Made. She finished with 2. We simply got lucky here with the volume as she only took 2 3PA.

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It was another 1-1 day. The first play was Olivia Miles Over 18.5 Points. She finished with 8 points on 3/12 shooting. She seemed very hesitant in this game and even though she had one on one looks, she tried to force bad passes. 5 of her 12 FGA were drives and another 5 were pullups (made zero FGA on pullups). Mid read here I’d say. The final play was Tiffany Hayes Over 9.5 Points. She finished with 14 points on 5/8 shooting in 18 minutes of action. She was 3/3 on drives as she doesn’t need to use PnR to attack off drives in halfcourt sets which is exactly what you want against a tough drive team. You want one on one drives.

Carla Leite Over 6.5 Assists (-135 Draftkings) 1u

In two games against the Wings, she’s had 8 and 11 assists on 15 and 19 potentials. With the big lineup the Wings have run, there is one big thing its giving up and its threes. In the last 7 games, The wings are allowing the 7th highest catch & shoot three point frequency at 29.6%. In the last two games, they’ve allowed the 3rd highest frequency at 33.8%. Now lets look at threes in general. The last 7 games they are allowing a 46% 3pt frequency. The last two games they are allowing a 54.1% frequency. In the last 7 games, 53.6% of Leite’s assists have come from threes. This interior defense makes it tough to score in the paint with three bigs and the data shows that with a 23% interior frequency (last two games vs 27% frequency the last 7 games which would also be 2nd best). I think this would be a good spot to spray kickouts on drives from her.

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We were a blowout hook away from a 2-0 day but instead it was a 1-1 day. The first play was Jessica Shepard Over 4.5 Assists who ended at 4 as she didn’t play the fourth quarter at all. If she plays the fourth I’m confident she goes over this line. The last play was Natasha Cloud Over 11.5 Points. She finished with 16 points. She had 66% of her FGA were drives and pullups which is exactly what we wanted. That alone accounted for 10 of her 16 points.

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Fewest Allowed

Most Allowed

Atlanta Dream

Chicago Sky

Connecticut Sun

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Chicago Sky

Connecticut Sun

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Chicago Sky

Connecticut Sun

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Chicago Sky

Connecticut Sun

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1-1 day here. The first play was Leila Lacan Under 12.5 Points. She finished with 13, hooking us due to insane foul trouble from ODO. If we look at Lacan with Griner, Edwards, and ODO off the floor per 28 minutes, she’s averaging 16.3 points. Can’t predict foul trouble unfortunately. Rhyne Howard Over 18.5 Points was our last play. She finished with 20, barely going over in the final 30 seconds of the game after scoring 12 points in the first. After that first quarter, the Mercury made it a priority to lock in on her with many doubles and help coming her way.

Jessica Shepard Over 4.5 Assists (-108 Fanduel) 1u

The system play didn’t work last game as she was able to repeatedly turn into a scorer, but I think with the size the Storm have, we should see her go back to being a passer here. Against the bottom 7 Transition defenses, she’s over this line in 13/17 games while averaging 6.2 apg on 10.8 potentials. The Storm are playing at the fastest pace in the league so this should benefit her with transition assists here. She last played the Storm back in late June where she had 8 assists on 13 potentials.

Natasha Cloud Over 11.5 Points (-118 Fanduel) 1u

Cloud has played well as of late and looks to continue that here as she gets a Fever team without Aliyah Boston to help on switches and protecting the paint. Last time they played was about two weeks ago where she went for 17 points. She did a great job of attacking the paint as she was 2/2 on drives and 2/2 on rim finishes (non drives or cut scores at the rim). In the last 7 games, the Fever are allowing the 7th highest drive rate and 6th highest rim rate. On top of that they are allowing the 4th highest pullup frequency, which is where almost 40% of her FGA have come from the last 7 games. Their rotations aren’t nearly as good without Boston on the floor so we should expect some positive looks for these Sky guards here whether spotting up or attacking the paint. Fast pace matchup as well. In games while facing the top 10 fastest paced teams (last 10 games data) with Vandersloot playing, she’s over this line in 8/11 games while averaging 15.1 ppg.

Disappointing 0-3 day. Bit of a slump the last few days here. The first play was Courtney Williams Over 14.5 Points. Finishes at 13 with 11 FGA, but of those 11, two of them came in the second half only. If she had at least half the the first half volume this is an easy winner. The second play was Sonia Citron Over 15.5 Points. She finished with 11 points. Poor read here with very low volume. The last play was Carla Leite Over 7.5 Assists. She finished with 6. Numbers aren’t out yet for her potentials but it should be a decent amount here in potentials.

Leila Lacan Under 12.5 Points (-128 Fanduel) 1u

She has not gone over this line yet without Griner or Edwards (0/7) and averaging 9.4 ppg without them. Lacan is averaging just 9.4 ppg without both vs 12.9 ppg with (minimum of 17+ minutes). In the last 7 games, 57% of her FGA have been pullups and the Sparks are allowing the 5th lowest pullup frequency. With how bad they are in the paint, This matchup fits Olivia Nelson-Ododa better as she gets a good chunk of her FGA in the paint and the Sparks are dead last there (highest frequency allowed).

Rhyne Howard Over 18.5 Points (-130 Fanduel) 1u

These high usage guards have torched the Mercury this season with players recently such as Ionescu (27), Mitchell (29), Leite (23), and Citron (19). She’s had 21 and 23 points against the Mercury already this season as they are prone to giving up threes and she’s knocked down 6 and 5 threes in these two matchups. When facing a bottom 7 defensive rated team and a non top 2 defense vs her position, she’s over this line in 12/15 games while averaging 21.3 ppg. She’s been shooting the three ball much better post all star break and hopefully that continues here in a good matchup.

0-2 day. Thought this should’ve been at least a 1-1 day with our first play being Erica Wheeler Over 8.5 Points. She had 8 points at the half and finished at 8 as she only played 5 second half minutes. The starters were pulled pretty early in this one. The Last play was Jessica Shepard Over 4.5 Assists. Huge scoring game from her so PA should’ve been the play but she was not involved in a ton of actions in this one as far as screening is concerned. Lots of one on one or just simply wide open or easy post up against guards.

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1-1 day. The first play was Kiki Rice Over 12.5 Points. She finished with a team high 19 points. She missed a good amount of the 2nd quarter but seemed planned since it was the 2nd leg of a back to back as nobody played 30+ minutes here. The last play was Courtney Williams Over 11.5 Points. She finished with 9 points. It was between her and McBride (as I mentioned in the writeup yesterday) to score based on the scheme and McBride went for 20 instead.

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2-2 day. The biggest thing for the plays here was the early injury to Jonquel Jones in the first quarter who we had for Over 1.5 Threes made. This in turn impacted the read of Kamilla Cardoso for Under 14.5 Points who ended up hooking but the defensive threat of Jones not being there impacted the entire read. Our third play was Olivia Nelson-Ododa Over 11.5 Points. This was an easy winner as she finished with a team high 18 points. The final play was Aneesah Morrow Over 18.5 Points + Rebounds. She finished with 30 Points + Rebounds.

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Terrible 0-3 day on Sunday. The first play was Angel Reese Under 12.5 Rebounds. She finished with 14 as overtime occurred. In games with 5 or less offensive rebounds, she’s under this line in 16/21 games. Next play was Natasha Cloud Under 10.5 Points. She finished with 20 points as they dared her to shoot and switched up the coverages from last week so bad read. The final play was DeWanna Bonner Over 6.5 Rebounds. Mack and Bonner shared the court mare than expected, yet she finished with 6 rebounds and 32 minutes of action. In games with 31+ minutes, she’s now over this line in 12/14 games.

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Chicago Sky

Connecticut Sun

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Chicago Sky

Connecticut Sun

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Chicago Sky

Connecticut Sun

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