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Bettor Up: Tools, Tips and Tricks

Bettor Up: Tools, Tips and Tricks

Clay Graham, Advantage Analytics,LLC, Las Vegas

Matt Turnipseede, MoneyLine Analytics,LLC, Las Vegas

Palisade Risk Conference – San Antonio – November 12, 2019

Reference Icon on Application

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Principles of Sports Gaming

The Pathway to Gaming Success

• Sports gaming as an investment

• What’s in a bet?

• Methods of wagering

–Micro tactics (Matt)

–Macro strategies (Clay)

• Bonus topic!

– Decoding Football’s Betting Lines

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Sports Gambling as an Investment(Objectives of Investment)

• Yield• Growth

– Profits• Internally generated• Compounding and reinvestment

– Capital Appreciation (equity)• Liquidity• Security• Risks

– Volatility– Capital 

What’s in a Bet?

• Basic types of wagers

–Moneyline

• Outright winner

– Over under 

• Total runs

• Set total score so (≈)equal chance: over or under

– Run line (spread)

• Scoring variance between teams

• Difference set so (≈) equal chance of winning margin

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Odds

Invest 100 to win 131 (moneyline)

Brooklyn gives up 9 points (spread)(implied odds = ‐110 dime line)

Fractional odds: bet 2 to win 7P(W) = 22%

Odds and Probability of Winning

Gaming Lines Conversions

Favorite Underdog

US Decimal Fractional ImpP(W) US Decimal Fractional ImpP(W)

‐100 2.000 1/1 50.0% 100 2.000 1/1 50.0%

‐105 1.952 20/21 51.2% 105 2.050 21/20 48.8%

‐110 1.909 10/11 52.4% 110 2.100 11/10 47.6%

‐115 1.870 87/100 53.5% 115 2.150 100/87 46.5%

‐120 1.833 5/6 54.5% 120 2.200 6/5 45.5%

‐125 1.800 4/5 55.6% 125 2.250 5/4 44.4%

‐130 1.769 77/100 56.5% 130 2.300 100/77 43.5%

‐135 1.741 37/50 57.4% 135 2.350 50/37 42.6%

‐140 1.714 71/100 58.3% 140 2.400 100/71 41.7%

‐145 1.690 69/100 59.2% 145 2.450 100/69 40.8%

‐150 1.667 4/6 60.0% 150 2.500 6/4 40.0%

‐155 1.645 13/20 60.8% 155 2.550 20/13 39.2%

‐160 1.625 5/8 61.5% 160 2.600 8/5 38.5%

‐165 1.606 61/10 62.3% 165 2.650 10/61 37.7%

‐170 1.588 59/10 63.0% 170 2.700 10/59 37.0%

‐175 1.571 4/7 63.6% 175 2.750 7/4 36.4%

‐180 1.556 14/25 64.3% 180 2.800 25/14 35.7%

‐185 1.541 27/50 64.9% 185 2.850 50/27 35.1%

‐190 1.526 53/100 65.5% 190 2.900 100/53 34.5%

‐195 1.513 51/100 66.1% 195 2.950 100/51 33.9%

‐200 1.500 1/2 66.7% 200 3.000 2/1 33.3%

Handout

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Selected Wagering, Winning Characteristics(percentages)

Winning Profile of Results by Type of Wagering 

Home team Favorite  Home Favorite Over Home Spread

Football 59 64 69 47 61

Basketball 59 67 78 51 51

Baseball 53 61 63 50 n/a

Source: https://sportsbookreviewsonline.com/scoresoddsarchives/season: NFL 2018‐19, Basketball 2018‐19, Baseball 2019

Tabulated by authors

MLB Scoring Distributions Aggregate(Negative binomial)

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MLB Value Volatility by Team 

Undervalued

Overvalued

Source: https://sportsbookreviewsonline.com/scoresoddsarchives/mlb/mlboddsarchives.htmTabulated by authors

NBA Home Court Advantage(% Home wins ‐ %Road wins) 

Source: https://sportsbookreviewsonline.com/scoresoddsarchives/nba/nbaoddsarchives.htmTabulated by authors

Mean home court advantage 18%

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Methods of Wagering

Micro Tactics ‐ Matt

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How it works ‐ BOARDS

• High volume of wagers per day

• Use probability of winning to select investments

• Six principal Sports

• Wide variety of investments (bets)

Sports Covered

• NHL Hockey

• NCAA Football

• NFL Football

• NBA Basketball

• NCAA Basketball

• MLB Baseball

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Wide Scope of Bets by Sport Example

NFL Football– 1st Q Spread– 1st Q Totals– 1st Q ML– 1st H Spread– 1st H Totals– 1st H ML– Spread– Totals– Moneyline– Dog Plays– Teasers– Parlays

NFL Control Sheet

Types of Bets

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Bet Progression System

5 $218.90 $199.00 $7.90

$108.0011/12/19 team b $118.80 $108.00

‐$56.1011/11/19 team a $56.10 $51.00

‐$26.4011/10/19 team z $26.40 $24.00

‐$12.1011/9/19 team y $12.10 $11.00

‐$5.5011/8/19 team x $5.50 $5.00

P/LDate Select Bet To Win

NFL 1st Q Spread

What if Loss

5 $218.90 $199.00 $7.90

$108.0011/12/19 team b $118.80 $108.00

‐$56.1011/11/19 team a $56.10 $51.00

‐$26.4011/10/19 team z $26.40 $24.00

‐$12.1011/9/19 team y $12.10 $11.00

‐$5.5011/8/19 team x $5.50 $5.00

P/LDate Select Bet To Win

NFL 1st Q Spread

5 $218.90 $199.00 ‐$218.90

‐$118.8011/12/19 team b $118.80 $108.00

‐$56.1011/11/19 team a $56.10 $51.00

‐$26.4011/10/19 team z $26.40 $24.00

‐$12.1011/9/19 team y $12.10 $11.00

‐$5.5011/8/19 team x $5.50 $5.00

P/LDate Select Bet To Win

NFL 1st Q Spread

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One Day of Business

MLB $0.00

NCAA F $0.00

NFL $47.90

NBA $47.90

NHL $47.90

NCAA B $47.90

$191.60

x 365

$69,934.00

**Ability to generate an 18‐20% return for $350k worth of business

Perspective

Description Value

Bets / Day 75

Average bets follow‐up to win 2.6

Return on average bet 6.8%

Smallest individual bet  5.50

Largest individual bet 120

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Burn out?

What’s he writing?

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Born to wager!

Macro Strategies ‐ Clay

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How it Works!

FOUNDATION FOR DECISIONS

Probability of winning P(W)

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Five Methods Utilized P(W)

I. Pythagorean 

II. Implied probability of winning

III. Match‐up

IV. Monté Carlo simulation

V. Logistic regression

I. Baseball’s Pythagorean Theorem 

A2 + B2 = C2

From Bill James who:

Identified non linear relationship between:

Runs scored and runs allowed1

Probability of winning = 

Runs Scored2 / (Runs scored2 + Runs allowed2)

Πυθαγόρας (our man Pythagoras)

Notes: (1) Derived value 2018 MLB season 1.77

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In a more general nomenclature:

Points Scoredα / (Points scoredα + Points allowedα)

Value of α varies with sport:

MLB Baseball:Runs = 1.77ΔOPS = 3.37

NBA Basketball = 14.39Football:

NFL = 2.91NCAA Division 1 = 3.33

Extrapolation to Other Sports

II. Implied Probability of Winning(derived from the Money line)

Game 3 ‐ October 25, 2019: 2019 World Series• Washington Nationals ‐141 (favorite)

• Houston Astros 131 (underdog)

P(W)Nationals = |‐141| / (|‐141| + 100) = 58.5% 

P(W)Astros = 100 / (131 + 100) = 43.2 %

Normalized to 100%:

58.5/101.8 = 57.5%

43.2/101.8 = 42.5%

Houston won 4 to 1

101.8%

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III. Variables Impacting Winning PercentageSorted by absolute value of the coefficient of correlation

Coefficient of Correlation: %Win with Selected Variables

Batting Pitching

Variable |%Win| Rank Variable |%Win| Rank

Δ(OPS) 0.933 1 Δ(OPS) 0.933 1

OBP 0.811 2 W 0.851 2

R 0.776 3 ERA 0.793 3

RBI 0.763 4 ER 0.782 4

OPS 0.758 5 OBP 0.764 5

Scr‐Al 0.706 6 OPS 0.753 6

SLG 0.692 7 BAA 0.736 7

BB 0.684 8 Scr‐Alw 0.706 8

TB 0.670 9 L 0.704 9

TPA 0.666 10 SLG 0.695 10

XBH 0.631 11 K/BB 0.656 11

AVG 0.621 12 SHO 0.641 12

ΔOPS Correlates More Highly With Winning Than Actual ΔScoring Differential !

StatTools ReportAnalysis: Correlation and Covariance

Performed By: Clay Graham

Date: Tuesday, October 15, 2019

Updating: Live

% Win Runs Scr Runs Alw Scr‐Al B‐OPS P‐OPS Δ(OPS)

Linear Correlation TableMLB Team 

StatsMLB Team 

StatsMLB Team 

StatsMLB Team 

StatsMLB Team 

StatsMLB Team 

StatsMLB Team 

Stats

% Win 1.000 0.776 ‐0.417 0.706 0.758 ‐0.753 0.933

Runs Scr 0.776 1.000 ‐0.185 0.632 0.971 ‐0.299 0.761

Runs Alw ‐0.417 ‐0.185 1.000 ‐0.878 ‐0.213 0.530 ‐0.470

Scr‐Al 0.706 0.632 ‐0.878 1.000 0.640 ‐0.563 0.741

B‐OPS 0.758 0.971 ‐0.213 0.640 1.000 ‐0.308 0.783

P‐OPS ‐0.753 ‐0.299 0.530 ‐0.563 ‐0.308 1.000 ‐0.832

B‐P(OPS) 0.933 0.761 ‐0.470 0.741 0.783 ‐0.832 1.000

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This is HUGE!

The OPS Matchup (ΔOPS) Correlates More Highly With 

Winning Than the Actual Scoring Matchup (ΔScoring Differential) !

Incorporating OPS into Match‐up ModelGame 3 World Series Astros at Nationals

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From Match‐up ModelResultant Probability of Winning 

OPS Pythagorean exponent = 3.374

Probability of Houston Astros winning game 3:

.78193.374 /(.78193.374 + .72043.374 ) = 57%

Houston won 4 ‐ 1

Expected OPS Houston Expected OPS Nationals

IV. Monte Carlo

• Build distributions of a teams scoring

– Road

– Home

• Build distributions of runs allowed

– Road

– Home

• Match‐up 

– Road scoring vs. Home runs allowed (2 distributions)

– Home scoring vs. Road runs allowed (2 distributions)

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Each Club has: 4 (Time Period Dependent) 

Negative Binomial Distributions

Negative Binomial Definition Matrix

(Partial List)

For Each Club: 4 Distributions(yep that’s 120 !) 

Angels at home runs allowedAngels at home runs scoredAngels on road Runs allowedAngels on road runs scored

Astros’ Match‐up ScoringMonte Carlo Simulation

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Nationals Match‐up ScoringMonte Carlo Simulation

Combining Astros and Nationals ExpectationsResultant Distributions

53.5%

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V. Logistic Regression Variables

• Strikeouts / Base on Balls Road

• Strikeouts / Base on Balls Home

• OBP + SLG (OPS) Road

• OBP + SLG (OPS) Home

Logistic Regression – Highly Accurate

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From Statistics to Ranks andMeasuring Competitive Advantage

Angels at MarinersScoring Allowed   Scoring Allowed28          23              1         11

Net Advantage Angels: (23‐28) + (1‐11) = ‐15

“Everything should be made as simple as possible, but not simpler.”

Albert Einstein

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Sizing Investment

Basic Objectives

• Maximize Profitability

– Total profits

– Return on investment (alternatively)

• Subject to:

– Probabilities of winning

– Payout

– Risks

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Specifically

Economically Exploit the

Market Inequities Between the 

Game (baseball) and the Betting Line

Selected Criteria – Economically Based

1. Expected payout

2. Level wagering (sizing)

3. Kelly criteria

4. Edge based

5. Probabilities of winning

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1. Expected Value Payout

• A function of the:

– Probability of winning

– Price of the line

• An example:

– Probability of winning bet = 60%

– Price of the bet

– Payout rate is simply an extrapolation of the Line

2. Level Betting

• Same amount for each bet (x% of bankroll / bet)

• Simple to calculate

• Cop out (don’t have a clue what to do?)

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3. Kelly Criteria(result = % bankroll to invest)

• Function of Fractional Odds and Probability of Winning

f = bp‐q/b = bp – (1‐p) / b  = {p(b+1) – 1} / bwhere:

f = fraction of bankroll to betb = net odds on wager, “b to 1”  win b for wager of 1p = probability of winningq = probability of loosing (1‐b) 

• In a more familiar termsFractional Odds = FO = (1/implied P(W)) – 1f = Kelly % bankroll = (P(W) * (FO‐1)) + 1) / FO

4. Edge Basis

Probability of winning > Implied Probability of Winning

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4. Edge Basis

Probability of winning > Implied Probability of Winning

5. Probabilities of Winning

• Player based– Batter pitcher matchups  

• Team Based– Exogenous

• Implied probability of winning

– Endogenous• Pythagorean • Monté Carlo simulation• Logistic regression

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All methods have Two Elements in Common

• Probability of winning

• Price of the bet – the odds, .eg., ‐150

Now what?Now what?

Build an Optimizing model

• Objective function

–Maximize profits

• Subject to:

– Convex combination of probabilities of winning

–Minimum odds, .i.e., > ‐135

– Combination of % Bankroll methods• Kelly criterion• Edge based• Payoff

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“Compound interest is the 8th wonder of the world. He who understands it, earns it; he who doesn't, pays it.” ...

Albert Einstein

Outcomes?

With optimal reinvestment

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2014 Results

BONUS TOPIC

Decoding Football’s Betting Lines

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Home – Road Point Differencemultiple regression formula (football)

StatTools ReportAnalysis:Regression

Performed By:Clayton

Date:Sunday, October 13, 2019

Updating:Static

Variable:H‐R

Multiple Regression for H‐R

Multiple

RR‐Square

Adjusted

R‐square

Std. Err. of

Estimate

Rows

IgnoredOutliers

Summary

0.5780 0.3340 0.3242 11.908589 199 0

Degrees of

Freedom

Sum of

Squares

Mean of

SquaresF p‐Value

ANOVA Table

Explained 5 24044.0 4808.8 33.9 < 0.0001

Unexplained 338 47933.3 141.8

CoefficientStandard

Errort‐Value p‐Value

Confidence Interval 95%

Regression Table Lower Upper

Constant ‐0.8047 6.0270 ‐0.1335 0.8939 ‐12.6598 11.0504

EVRd 0.4099 0.2425 1.6899 0.0920 ‐0.0672 0.8870

EVHm ‐0.0749 0.2393 ‐0.3130 0.7544 ‐0.5457 0.3958

Δrank 0.3628 0.0432 8.4005 < 0.0001 0.2778 0.4477

MLDecHm ‐2.4547 0.9786 ‐2.5083 0.0126 ‐4.3797 ‐0.5297

HmAdv ‐22.3901 13.1668 ‐1.7005 0.0900 ‐48.2894 3.5091

Spread & totals

Historic dataDecimal moneyline

P(W) moneyline‐P(W) spread totals

What Can be Learned form Football Lines?(probability of winning from Spread and Totals)

Integrating the  Spread and TotalsSpread = ‐Pointshome + PointsroadTotals = Pointshome + Pointsroad

Solving for points scoredPh   = (Totals + Spread) / 2 Pr = Totals – Ph

Applying Football’s Pythagorean

P(W)S,T = (Ph)2.91 / ((Ph)

2.91 + (Pr)2.91 )

2 equations 2 unknowns

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What Can be Learned from Football Lines?

From the Money Line

If ML< 0

P(W)ML ≈ |ML| / (|ML| + 100)

If ML>0

P(W)ML ≈ 100 / (ML + 100)

MEANINGS?

What if:

P(W)ML   < P(W)S,T

What if:

P(W)ML   > P(W)S,T

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Home Advantage from Odds(Year to date 58%)

Winning 61%  Winning 65% 

How’s it Working – weeks 4 through 8

Week Spread Parley Teaser

4 67 20 75

5 67 25 25

6 40 0 14

7 100 100 100

8 80 67 67

Win% 71 42 56

BE line ‐242 136 128

Actual Line ‐110 264 ‐120

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Combining Home Advantage with Expected Run Differential

Week Spread Parley Teaser

4 67 20 75

5 67 25 25

6 40 0 14

7 100 100 100

8 80 67 67

9 13 0 18

10 40 0 11

Win% 58 30 44

BE line ‐139 230 126

Actual line ‐110 264 ‐120

https://www.youtube.com/watch?v=2I91DJZKRxs

“You’re gonna need a bigger boat!”(Learn from history) 

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“We’re gonna need a bigger bank!” 

Stay away from negative people, they have a problem 

for every solution.Albert Einstein

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