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How DMP Works

Under the hood: projections, EV, and insights

DMP picks props through a simple, three-step process: build a baseline projection, adjust for today's context, then only recommend bets where the price gives us positive expected value. Most props get filtered out. The handful that survive have multiple independent reasons to be there.

The 3-Step Process

Every prop on DMP goes through the same three steps. The earlier sections of this page explain each step in depth. Here's the high-level shape of how a prop earns a recommendation.

1

Projection

Every prop starts with a player-level projection — an expected value and an uncertainty range, generated by a sport-specific simulator.

  • NBA: Monte Carlo for points, rebounds, assists, threes, and combos.
  • MLB: PA-level batter and BF-level pitcher Monte Carlo for hits, total bases, Ks, HRs.
  • NCAAB: Weighted rolling baseline with skew-corrected distributions.

Output is calibrated per (sport, market) using isotonic regression on historical resolved bets, so a stated P(hit) = 60% actually wins 60% of the time.

2

Context

The baseline projection gets shifted up or down based on what's actually happening today. Sport-specific factors only — no generic catch-all.

  • NBA: injury redistribution by archetype, pace, defense-vs-archetype.
  • MLB: lineup, platoon, pitcher matchup, park, weather.
  • NCAAB: pace, home court, archetype redistribution.

Context is where the bulk of the analysis happens. A correct baseline against the wrong context is still a losing bet.

3

+EV Gate

A prop only becomes a recommendation if the price the book is offering gives us positive expected value against the calibrated probability.

  • EV > 0 against the offered price
  • Projection edge > 0 in our direction
  • Calibrated probability inside a sane range
  • Market is well-calibrated (low ECE)
  • Top-tier rank on EV and projection edge for today's slate

Hard gates, not soft scores. A prop that fails any single check is filtered out.

Why most props get filtered out

On a typical NBA slate we look at thousands of player-prop lines. After steps 1 and 2, many have a defensible projection. After step 3, only a small fraction survive — usually ten to thirty bets per night across all leagues. The point isn't to be right about every prop. The point is to be disciplined about when we play.

How we measure ourselves

Paper-trading numbers are easy to fake and easy to fool yourself with. We track five metrics on every Recommended Bet — each one catches a different kind of failure. You can see them live on the public model performance dashboard.

CLV

Closing Line Value

Did the market agree with our pick? When sharp money flows the same way we bet, the line moves toward our side. Positive CLV across many bets is evidence of real edge — it means we're consistently picking the side the market eventually agrees with.

β

β coefficient (model vs market)

How much of the closing line's movement is because of our model's information vs. other reasons? We regress the closing line on the open price and our model's prediction. Positive β = our model knows things the market eventually figures out. This is the test casinos use for in-house models.

Brier

Brier score

How accurate are our probability predictions? Low Brier = when we say a prop has a 60% chance, it actually happens about 60% of the time. Catches over-confidence: a model that screams "90% lock!" on bets that hit 55% has a great hit rate but a terrible Brier score.

z-score

Predictive signal strength

Are our predictions statistically distinguishable from random guessing? We shuffle the win/loss column 100 times and re-score. A strongly negative z (z < −3) means our real predictions match outcomes much better than random shuffles ever did — actual signal beyond chance.

ROI

Return on investment

The bottom line: do paper-traded picks make money? The other four metrics are leading indicators — they tell us whether the model has real edge. ROI is the lagging confirmation. The tracked record assumes flat 1-unit bets at the best available odds, with every bet frozen at first qualification (no cherry-picking).

Why all five? Each one catches a different kind of failure. A model can have positive ROI by luck, positive CLV without real edge, or great Brier scores while still losing money. Tracking all five is how we tell variance from genuine performance — and how we know when something starts breaking before the bottom line confirms it.

Projections

Every prop you see on DMP has a projection behind it -- a statistical estimate of what a player is likely to produce in their upcoming game. Projections power two things: the PROJ column on the odds screen and the P(over) probability used for model-based EV. Each sport has its own pipeline.

Stats We Project

PointsReboundsAssists3-Pointers MadePTS+REB+ASTPTS+REBPTS+ASTREB+AST

Combo stats (PRA, PR, PA, RA) use covariance modeling -- they account for how stats correlate within the same game, not simple addition.

1Weighted Baseline

DMP starts with a player's real game logs and computes per-100-possession rates for each stat across three time windows. This normalizes for pace so a player in a fast game and a slow game are compared apples-to-apples.

NBA Weights
  • Last 5 games: 50%
  • Last 10 games: 30%
  • Season: 20%

Recent form weighted heavily. NBA roles shift fast.

2Matchup Adjustment: 10-Archetype DVP

Players are classified into archetypes based on usage, assist rate, and play style -- primary ball handler, stretch big, rim runner, and seven others.

DVP is calculated per-archetype, per-stat. A team might be tough on scoring wings but soft against stretch bigs. Each stat gets its own softness score (0-100 percentile).

3Context Adjustments

Pace

Faster-paced games create more possessions and more statistical opportunities. DMP uses actual possession data when available, or derives pace from Vegas totals.

Home Court

+2% home boost applied to projections.

Injury Redistribution

When a teammate is OUT, their statistical opportunity flows to remaining players. DMP models this using archetype-based redistribution rules. A primary ball handler going out boosts combo guards differently than it boosts rim runners.

Each injured player has an impact tier (A through D) based on their minutes and usage. A Tier A star (30+ MPG, high-usage) missing redistributes far more than a Tier D bench player.

Skill Curves: The Role Player Trap

More opportunity doesn't always mean more production. Informed by Dean Oliver's research in Basketball on Paper, DMP models each player's skill curve — how their efficiency changes as their usage increases.

Stars like LeBron and Curry have flat curves — they maintain or even improve their efficiency when handling more of the offense. Role players have steep curves — when forced into a bigger role, they take harder shots, commit more turnovers, and their efficiency collapses.

DMP computes this from game logs: bucketing each player's games by usage rate and measuring their per-possession efficiency at each level. The result is a dampening factor that adjusts redistribution boosts downward for role players.

Flat Curve

Stars absorb load

Full boost

Moderate

Starters handle some

Partial boost

Steep Curve

Role players collapse

Dampened boost

This prevents the "role player trap" — where the market overvalues a bench player's OVER prop after a star injury, not accounting for the efficiency decline that comes with increased usage.

3bStat-Specific Injury Leverage

Beyond the projection boost, DMP computes a separate injury leverage signal for scoring that uses each injured teammate's specific stat shares — the percentage of the team's points, rebounds, assists, and 3PM they normally produce — rather than a single flat impact number.

Why it matters: a star point guard going out redistributes assists more than rebounds. For a Rebounds prop, leverage comes from the injured teammate's reb_share, not their overall impact. Combo props (P+R, PRA, etc.) sum the relevant stat shares. Previously the model used a single Points-biased impact multiplier across all markets, which under-rated injury leverage on Rebounds props and over-rated it on Points props.

4Six Context Signals

On top of the projection, DMP computes six additional signals that capture qualitative edges the projection can't — hot/cold streaks vs. expected production, game script alignment, expanding/contracting roles, fresh injury opportunities, factor stacking, and teammate-pair fit.

1. Hit Rate Value

Compares the player's actual L10 hit rate at today's line vs. the model's P(hit). If a player has gone over 26.5 points in 8 of their last 10 games but the model only gives P(over) = 60%, the market may be missing a hot streak — or the hot streak may be unsustainable. The gap itself is the edge signal.

2. Script Sensitivity

Compares a player's stat production in competitive (spread < 8), mid (8–15), and blowout (15+) games. Some players thrive in close games; others rack up empty-calorie numbers in blowouts. DMP buckets today's game by Vegas spread and pulls the player's historical production in that bucket, measuring how much it deviates from their season average. Side-aware: a script-dependent scorer on the OVER in a projected blowout gets a positive signal if they historically score more in blowouts.

3. Role Trend

Measures whether a player's role is expanding or contracting. Combines L5 vs. season minutes and L5 vs. L10 usage. A rookie getting a starter bump, a veteran losing minutes to a trade deadline acquisition — role changes often precede market line moves by multiple games. Capped at ±1.0 to avoid G-League callup outliers.

4. Injury Freshness

Fresh injuries (0–3 games) are worth more than stale ones (15+ games) because the market has had less time to price them in. DMP weights each teammate's absence by how recent it is: 1.0 for brand-new (Q→OUT downgrades same day), 0.5 for ≤7 games missed, 0.1 for long-term absences. Side-aware: a fresh absence helps the player's OVER, hurts their UNDER.

5. Factor Alignment

Checks how many of the other context factors (DVP softness, pace, injury leverage, role trend, script sensitivity) agree with the current bet direction. When they all point the same way, the bet has converging context. When they contradict each other, the bet is fragile — flagged so we don't pick something where only one factor is strong and everything else is neutral or against it.

6. Teammate Fit

Pairwise lineup fit from season game logs: which teammates does this player produce MORE with and which do they produce LESS with? When a high-fit teammate is absent, the player's OVER is at risk even if the raw injury leverage says otherwise. Inspired by Dean Oliver's pairwise teammate analysis in Basketball on Paper.

5Variance and P(over): Skew-Corrected Distributions

DMP computes variance (how spread out a player's outcomes are) from recent game-to-game consistency. But variance alone isn't enough — NBA stats are right-skewed, meaning the mean and the median aren't the same number. Sportsbooks price lines at the median, but a naive Normal distribution assumes mean = median and systematically overestimates P(over).

Cornish-Fisher Skew Correction (Points, Rebounds, Assists, Combos)

DMP uses a Cornish-Fisher expansion on the z-score to correct for skewness before applying the Normal CDF. Empirical skewness coefficients from historical game log analysis: Points 0.35, Rebounds 0.55, Assists 0.65, combos 0.40–0.55. For a player projected at 22.5 pts with variance 50 against a line of 22.5, naive Normal returns P(over) = 50.0%. Skew-corrected returns 46.8%. That 3.2-point gap is the difference between a positive-EV and negative-EV bet.

Negative Binomial (3-Pointers Made)

3PM is a count stat with overdispersion — variance is typically 1.5–2× the mean because of hot/cold streaks. Poisson assumes variance = mean and therefore under-represents tail risk on tight lines. DMP uses a Negative Binomial CDF parameterized by the observed variance, which correctly models the wider-than-Poisson distribution.

6Shrinkage (Regression to Mean)

After all adjustments, DMP blends the adjusted projection (70%) with the player's season average (30%). This prevents the model from overreacting to a hot or cold streak and keeps projections grounded when context adjustments stack aggressively.

Expected Value (EV)

Expected value tells you whether a bet is priced in your favor. DMP calculates EV differently by sport because the data available to price each market is fundamentally different.

NBA

Sharp Book Consensus

For NBA props, sportsbooks like Pinnacle, Circa, and Bookmaker post lines with tight margins. These "sharp" books are considered the closest reflection of true probability.

How it works:

  1. DMP collects odds from 5 sharp books (FanDuel, Caesars, Pinnacle, Circa, Bookmaker)
  2. Each book's odds are devigged -- the house edge is removed to extract the implied true probability
  3. A consensus probability curve is built from all sharp book lines
  4. For each prop at each sportsbook, EV is calculated: does the offered price exceed the consensus fair price?

The +EV% you see for NBA props represents how much the offered odds exceed the sharp book consensus. This is a pure market-based approach -- it trusts that sharp books have priced the true probability correctly.

MLB

Sharp Book Consensus

MLB uses the same sharp book consensus approach as NBA. The same 5 sharp books (Pinnacle, Circa, FanDuel, Caesars, Bookmaker) are devigged and combined into a consensus probability curve. EV represents how much the offered odds exceed this consensus.

MLB prop markets are liquid enough for sharp books to price accurately, making the market-based approach reliable without needing a model blend.

NCAAB

Model + Market Blend

Sharp books rarely post college basketball player props. Without sharp lines to anchor a consensus, DMP uses a dual approach that combines its own model with the retail market.

How it works:

  1. A consensus is built from 14 retail books (FanDuel, DraftKings, BetMGM, Caesars, Bet365, and others)
  2. Each book is evaluated using leave-one-out -- its own line is excluded from the consensus when calculating its EV, preventing self-reference bias
  3. DMP's projection model independently calculates P(hit) from the player's projected stats and variance
  4. The final EV blends: 60% model probability + 40% market consensus

When market data exists (2+ books)

Blended EV gives you both the model's view and the market's view, weighted toward the model. If they disagree by more than 8%, the EV chip shows an amber warning.

When only 1 book posts a line

No consensus is possible. EV falls back to pure model EV -- 100% from DMP's projection. These are higher-conviction but unconfirmed by the market.

Why blend? Retail books still carry signal (real money is wagered), but they're less precise than sharp books. The model fills the gap with statistical projections.

The EV Formula

Regardless of how the probability is determined (sharp consensus or model blend), the EV formula is the same:

EV = P(hit) x Payout - (1 - P(hit))

A positive EV means the offered odds imply a probability lower than the true probability. Over enough bets, positive EV plays are expected to profit.

AI Insights

Projections and EV tell you whether a bet has mathematical value. AI Insights answers a different question: is today an outlier game or a typical game? It evaluates whether the statistical edge is durable (likely to persist) or fragile (likely to evaporate due to context).

Six Factors Evaluated

1

Role & Minutes

Are minutes locked in or volatile? Is usage trending up or down? High stability = durable edge.

2

Game Script

Vegas total and spread predict pace and blowout risk. High totals favor OVER; large spreads threaten early benchings.

3

Matchup (DVP)

Stat-specific defensive data. A team can be tough overall but soft on a specific stat. The AI evaluates each separately.

4

Injuries & Skill Curves

Tier-weighted injury impact plus skill curve analysis. The AI considers whether the absence is new or established, AND whether the beneficiary player can actually convert increased usage efficiently. Role players with steep skill curves are flagged as "role player traps" — more opportunity but worse efficiency.

5

Line Movement

Is the line moving with or against you? Movement direction signals whether sharp money or soft action is driving the price.

6

Model Output

The projection, P(over), fair odds, and market classification provide the statistical baseline the AI evaluates against.

League-Specific Context

NBA

The Redistribution Framework

NBA AI Insights follow the Redistribution Game — a 4-step sequential analysis for every prop:

1
The Role

Archetype, usage rate, minutes stability, skill curve classification (flat/moderate/steep). Establishes the baseline.

2
The Redistribution

Injury tiers (A-D), new vs established absences, redistribution flow by archetype, skill curve check, role player trap detection.

3
The Game

Pace (Vegas total), blowout risk (spread), game script, minutes projection, home/away boost.

4
The Defense

DVP by archetype with per-stat softness (PTS, REB, AST, 3PM separately), defender injuries, defensive scheme analysis.

NCAAB

What the AI considers

  • 40-minute game context: Stats are ~17% lower than NBA. Per-40 normalization is used for comparison.
  • Team shooting scheme: Whether the team is perimeter-heavy (40%+ of shots from 3) or inside-oriented -- directly impacts 3PM ceilings
  • Opponent defensive pressure: High-pressure defenses (70th+ percentile in steals/forced TOs) suppress assists for ball handlers
  • Foul risk: 5-foul limit means a player averaging 2.5+ fouls/game has real minutes variance
  • Conference context: Conference games are well-scouted (DVP more reliable). Non-conference games carry more uncertainty.
  • Neutral sites: Tournament games have no home boost -- affects all player projections
  • Player role classification: Primary Ball Handler, Key Creator, Key Scorer, Rotation, or Role Player -- derived from position, tier, and usage
MLB

The Conditions Framework

MLB AI Insights follow the Conditions Game — a 4-step sequential analysis for every prop:

1
The Arm

Start with the pitcher. K rate, FIP (not ERA), pitch count expectation, FIP-ERA gap. For batter props, the opposing pitcher sets the ceiling.

2
The Matchup

Platoon advantage (L/R handedness — 20-30% swing), pitcher vs batter archetype clash, catcher framing for K props, lineup K tendency.

3
The Environment

Park factors (granular by handedness), weather (wind, temperature), lineup position (PA count), game total, umpire zone tendency, time of season.

4
The Number

Regression signals: BABIP deviation from career, FIP-ERA gap, xwOBA vs wOBA gap, LOB% vs 72%. Is the market pricing the average or today's conditions?

What the AI Prioritizes by Prop Type

Different stats are driven by different factors. The AI evaluates each prop type through its own lens:

Points

NBA Pace, DVP archetype, usage trend, blowout risk. NCAAB Tempo is the #1 driver (70+ possessions = high ceiling, <64 = compressed), then per-40 rate, DVP, foul risk.

Rebounds

Position stability, opponent FG% (more misses = more boards), game script (competitive games keep starters in), pace. Blowouts compress rebound opportunities as benches come in.

Assists

Ball-handler role and usage rate are primary. NCAAB Opponent defensive pressure is key for handlers -- disruptive defenses force turnovers instead of assists. Teammate shooting % also matters (better shooters = more assist opportunities).

3-Pointers Made

Higher variance than other stats. The AI flags when L10 average is above the line but hit rate is below 50% -- a sign that one or two hot games are inflating the mean. NCAAB Team shooting scheme is the primary driver (attempt ceiling matters more than accuracy).

Combos (PRA, PR, PA, RA)

Highly minutes-dependent -- blowout risk is the #1 threat. Pace matters most since more possessions lift all component stats. The AI also considers stat correlation (playmakers have correlated PTS+AST).

Pitcher Strikeouts MLB

The cleanest MLB prop. Pitcher K% (stabilizes in ~70 BF) × opponent lineup K tendency. Catcher framing adds ±1-2 K. Poisson distribution. Reliable from Opening Day.

Hits & Total Bases MLB

Contact rate and ISO (power) vs opposing pitcher K% and FB%. Park + weather stack is the biggest swing factor. BABIP regression is THE edge signal — don't trust April hot streaks.

Home Runs MLB

Highest variance MLB prop. Park + weather + platoon can swing probability ~30%. Only bet 5%+ HR/PA hitters. Price is everything — binary event, Bernoulli distribution.

Batter Strikeouts MLB

Most reliable early-season prop. K% stabilizes at ~60 PA. Combine pitcher K% × batter K% for matchup projection. Platoon disadvantage adds 3-5% K rate.

The Verdict System

After evaluating all six factors, the AI delivers one of five verdicts:

VALUE
EV Durable

All factors aligned. Edge is structural, not situational.

LEAN
Mostly Durable

Strong structural support with one minor uncertainty.

SHOP
Strong Fundamentals, Wrong Price

The thesis is sound but you need better odds. Look for a better line elsewhere.

WAIT
Pending Status

Fundamentals are strong but a key variable (injury status, starting lineup) is unresolved.

PASS
Fragile Fundamentals

The thesis itself is weak -- the edge exists on paper but context undermines it.

Underdog Slips

DMP auto-generates optimized multi-leg Underdog Pick'em slips by combining our +EV-gated recommended bets with correlation modeling, Monte Carlo simulation, and payout structure optimization. Every slip is designed to maximize expected value while accounting for how legs interact with each other.

NBANCAABNBA and NCAAB only for now

Why Auto-Generated Slips?

Building a good multi-leg slip isn't just picking your best bets and combining them. Legs from the same game are correlated -- if one hits, the other is more likely to hit (or miss) too. A slip with correlated legs has a different probability of winning than what you'd calculate by multiplying individual leg probabilities. DMP models these correlations explicitly and optimizes slip construction against Underdog's actual payout tables so every slip is structured for maximum edge.

How Slips Are Built

1

Pre-Filtered Leg Pool

Slip legs are drawn from the same pool that powers recommended bets — every leg has already cleared the +EV gate: calibrated P(hit) beats implied probability, edge clears the per-market floor, and the prop ranks well on today's slate. This means every leg in every slip starts with a real edge before correlation modeling even begins.

2

Market Fair Probability

For EV calculation, we use the consensus devigged probability at Underdog's specific line, not our model projection. This probability is derived from a consensus curve built across 5 sharp sportsbooks (Pinnacle, Circa, FanDuel, Caesars, Bookmaker), devigged to remove vig. When UD offers a softer line than the market consensus, the fair probability is higher -- and the EV reflects that edge accurately.

3

Greedy Slip Construction

DMP builds candidate slips across multiple sizes (2-6 legs) using greedy optimization. Each slip must satisfy hard constraints: max 2 legs per game, min 2 unique teams, no incompatible stat markets for the same player (e.g., Points and Points+Rebounds+Assists -- since one is a component of the other), and max 2 legs per player. A Jaccard diversity guard ensures no two slips overlap more than 60%, so you get meaningfully different options.

4

Gaussian Copula Monte Carlo

This is the key step. Rather than assuming legs are independent (which would overestimate or underestimate joint probabilities), DMP runs a 10,000-sample Monte Carlo simulation using a Gaussian copula model. The correlation matrix accounts for: same-player cross-stat covariance (recovered from our projection variances), teammate correlation (shared pace and game script), same-game opposing-team correlation (shared game total), and bet direction -- two Overs on teammates are positively correlated, but an Over and an Under on teammates are negatively correlated. The simulation outputs P(exactly k legs hit) for k=0 to n, which feeds directly into the payout calculation.

5

Payout Structure Optimization

Underdog has two payout modes: Standard (all legs must hit, higher payout) and Flex (allows 1-2 misses, lower payout per tier). DMP computes EV for both modes using UD's exact payout tables -- Standard uses P(all hit), while Flex sums across all payout tiers weighted by P(exactly k hits). We recommend whichever mode has higher EV for each slip and show both when applicable.

6

Multiplier Confirmation

Underdog applies per-leg multipliers that adjust the payout (e.g., 0.85x for a popular pick, 1.12x for a less popular one). Since these multipliers aren't available in our data feed, you enter them from the UD app when reviewing a slip. DMP recalculates the true EV with your actual multipliers and tells you whether the slip is still playable -- because a +15% EV slip can become -EV after a heavy multiplier discount. You can also adjust lines if UD's current line differs from what we have, and we'll re-interpolate the fair probability from the consensus curve.

Why Correlation Matters

Without Correlation Modeling

Multiplying individual leg probabilities assumes legs are independent. If LeBron Over Points and AD Over Rebounds are in the same slip, an independent model treats them separately -- but in reality, a Lakers blowout win boosts both.

P(all hit) = 0.55 x 0.60 = 33.0%

With Correlation Modeling

The Gaussian copula captures that both outcomes are driven by the same game state. The correlated simulation accounts for scenarios where both hit or both miss more often than chance would suggest.

P(all hit) = 36.2% (copula-adjusted)

The direction of each bet matters too. Two Overs on teammates are positively correlated (good game lifts both). But an Over and an Under on teammates are negatively correlated (contradictory expectations reduce joint probability). DMP encodes this directional awareness into the correlation matrix before running simulations.

Risk Signals

Each slip includes fragility badges so you can assess downside risk at a glance:

Minutes Risk

A player in the slip is on the injury report as questionable or doubtful. If they play limited minutes, the leg could miss regardless of projection accuracy.

Blowout Risk

The Vegas spread is 10+ points. Starters may sit in the 4th quarter, capping stat accumulation and increasing variance.

Concentrated

More than half the legs come from the same game. The slip's outcome is heavily tied to one game's flow rather than being diversified.

League Comparison at a Glance

AspectNBANCAABMLB
Game Length48 minutes40 minutes9 innings (~3 hrs)
Matchup System10 archetypes, per-stat softness3 position buckets (G/F/C)Pitcher-vs-batter, platoon splits
P(over) DistributionNormal CDFNormal CDFNormal, Bernoulli, or Poisson
Baseline MethodWeighted L5/L10/seasonWeighted L5/L10/seasonRolling rates + stabilization regression
Home Boost+2%+3.5% (neutral sites: 0%)Park factors (venue-specific)
EnvironmentN/AN/AWeather, wind, temperature
InjuriesFull tier system, redistributionNot tracked (no reliable data)Lineup confirmation (starter identity)
EV MethodSharp book consensusModel + market blend (60/40)Sharp book consensus
Key Edge SignalInjury redistributionTempo + scheme mismatchBABIP regression
AI Insights FocusInjuries, redistribution, defender matchupsTempo, pressure, scheme, foul riskPlatoon splits, BABIP/xwOBA regression, park, weather

Key Takeaway

Each sport gets a purpose-built projection system tuned to its realities. NBA has the richest data (archetypes, injuries, defense profiles), so the system is the most granular. NCAAB compensates for thinner data with heavier season weighting, model-based EV, and context-specific adjustments like foul risk and defensive pressure. MLB is fundamentally different -- pitcher-vs-batter matchups, platoon splits, park factors, and weather create a projection pipeline that shares the same philosophy (quantify edge, model variance, compute P(over)) but uses entirely different inputs and statistical distributions.

The numbers on the odds screen -- PROJ, +EV%, and AI Insights -- each answer a different question. The projection says "what should happen."EV says "is this bet priced in your favor."AI Insights says "will today's context let it happen."And Underdog Slips say "here's how to combine the best edges into optimized multi-leg plays."

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