bettor up: tools, tips and tricks · updating:live % win runs scrruns alw scr‐al b‐ops p‐ops...
TRANSCRIPT
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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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