# How is the HoldCrunch Percentage Calculated?

The HoldCrunch Percentage represents ‘the distance between Sportsbooks on price’, with 0% being the best price and 100% the worst. If Book A is 5% HoldCrunch, Book B is 6% and Book C is 35% then it is clear at a glance that Books A and B are offering similar prices but Book C is less competitive

The HoldCrunch Percentage is calculated by identifying the leader on price on the lines and markets where customers put their money, in any one situation, such as pre-game or in-game, and the distance of another Sportsbook from that price

We use moneyline, spread and totals markets for NBA, NCAAB, MLB, NFL and NCAAF (excluding pre-season games) which represent 45% to 55% (depending on time of year) of handle across all sports and bet types. The HoldCrunch Percentage is therefore representative of the price experience of a significant proportion of customers by volume. Here’s an example of how we calculate the HoldCrunch Percentage for ‘Sportsbook X’:

#### Data point 1 – Book X’s current price vs the leader(s)

Book X’s current price on average for the markets and lines where customers put their money | Average price of the leader(s). We say ‘leaders’ because different books might lead on price at different times. Book X might also be the leader at one or other point in time |

-110.5 | -109 |

#### Data point 2 – Calculating Book X’s HoldCrunch Percentage

Next we calculate the ‘worst price boundary’ which is where HoldCrunch would normally be 100%. To do this we identify the worst prices in the markets and lines where customers put their money, pre-game and in-game. In this example we’re saying the average of all the worst prices is -113.5 | Now we can calculate Book X’s HoldCrunch Percentage. This is where it’s average price of -110.5 sits between the leaders, -109 and the ‘worst price boundary’, -113.5. Book X’s HoldCrunch Percentage is 1.5 (the distance to the leader) / 4.5 (the total distance between the leader and the worst price boundary) which is ~33%* | Average price of the leader(s) | |

Average price | -113.5 | -110.5 | -109 |

HoldCrunch % | -100% | ~33%* | 0% |

*The actual HoldCrunch formula is not a linear percentage reflection of where a book sits between the leader and the worst price boundary because handle may be less vulnerable to movement if a book is closer to the leader. These examples use a linear HoldCrunch Percentage illustration to more easily explain the principle behind the formula

## Methodology for applying the HoldCrunch Percentage to GGR to get GGR+

In order to apply the HoldCrunch Percentage as a margin cost for competing on price we must first take a step back and understand what lies behind a Sportsbook’s GGR/NGR performance. We’ll start with GGR and then come back to NGR. Here are some assumptions for what drives ‘current GGR performance’ for our Book X:

- As we’ve seen in the table above, customers of Book X see an average price of -110.5 on both sides of their wagers
- We’re going to assume the true probability of each bet is 50%, so the zero margin odds would be 100 on both sides, just like the toss of a coin
- No trading team is so good that they calculate true probability perfectly for every sporting event or incident within a sporting event in their models. Book X’s margin is therefore not ‘10.5’, the profit margin part of -110.5 when expressing margin as a price
- We’re going to assume that Book X actually keeps half of its theoretical margin, or in industry language ‘Book X holds 50% of the overround’, so Book X’s real gross margin expressed as a price is -105.25. -105.25 as a more familiar GGR percentage is 5% (5.25 divided by 105.25)

Here’s the methodology summarised in a table:

#### Book X’s current GGR performance

Book X’s current price on average | Book X’s actual margin. Nobody’s models are so perfect that they keep all of the overround all the time. In this example we’re saying Book X holds 50% of the overround/theoretical margin | Book X’s actual margin expressed as an average price |

-110.5 | 50% | -105.25 |

#### Applying the HoldCrunch Percentage to GGR to get GGR+

Book X’s current margin expressed as an average price | Book X’s HoldCrunch Percentage | Book X’s GGR+ (GGR after equaling the leaders on price): -105.25 minus 33% = -103.5 |

-105.25 | 33% | -103.5 |

## Methodology for applying the HoldCrunch Percentage to NGR to get NGR+

To calculate ‘NGR+’, NGR after taking account of the cost of competing on price, not just promo spend, simply add the reported promo spend percentage to the HoldCrunch Percentage. Here’s an illustration:

#### Applying the HoldCrunch Percentage to NGR to get NGR+

Book X’s current GGR margin expressed as an average price | Book X’s HoldCrunch Percentage | Book X’s reported promo spend as a percentage of GGR | Book X’s NGR+ (margin after the cost of equaling the leaders on price, and the cost of promo spend): -105.25 minus 73% = -101.42 |

-105.25 | 33% | 40% | -101.42 |

Here’s the same NGR+ calculation using the more familiar GGR and NGR percentages rather than margin expressed as a price:

Book X’s current GGR: 5.25 / 105.25 = 5% | Book X’s HoldCrunch Percentage | Book X’s reported promo spend as a percentage of GGR | Book X’s NGR+: 5% minus 73% = 1.35%. Note how inaccurate NGR can be. NGR would be reported as 3% (5% minus 40%), but Book X’s true margin while sustaining handle share is in fact 1.35% |

5% | 33% | 40% | 1.35% |

## Why the HoldCrunch Percentage is an accurate calculation for the cost of competing on price

Book X’s HoldCrunch Percentage is 33% based on where it sits between the best and worst prices, but why is 33% an accurate calculation of the cost of competing on price in our example, not just an expression of Book X’s distance from the leader(s)? Here’s a table to explain why:

Book X’s margin expressed as an average price | Number of price points Book X would have moved if it had equaled the leader(s), i.e. the difference between -110.5 and -109 | Assuming Book X took the same bets at -109 rather than -110.5, it’s margin would have been -103.75 (-105.25 reduced by 1.5) because this 1.5 price movement is the margin it would have forfeited |

-105.25 | 1.5 | -103.75 |

This margin answer above, -103.75 is almost identical to the -103.5 in the table below which uses the HoldCrunch Percentage methodology for the same scenario:

Book X’s current margin expressed as an average price | Book X’s HoldCrunch Percentage | Book X’s margin when equalling the leader(s) on price: -105.25 minus 33% = -103.5 |

-105.25 | 33% | -103.5 |

The HoldCrunch Percentage is an accurate calculation of the cost of competing on price because the HoldCrunch formula understands the relationship between price movements and margin deductions. Note too that the % of overround held is not a fixed number in practice. Book X’s margin would not be 50% of -109, it is likely to be less. The reason is that Sportsbooks have a spectrum of customers and keener prices will attract different volumes and skill levels. This is why we’ve tested our assumptions against a range of price points, price boundaries and levels of overround held. An illustration can be found here

## Why the HoldCrunch Percentage applies to Parlays as well as Straight bets

The HoldCrunch Percentage is calculated using straight bet data, but it is absolutely applicable as a reduction factor to parlay margins because parlays are more often than not a series of straight bets. This relationship between straight bets and parlays is why the HoldCrunch Percentage can be used with state reports that are a combination of both bet types. Here’s an example to illustrate why parlay margins are directly influenced by straight bet margins:

#### Step 1 – Calculating Book X’s straight bet margin

Book X’s average price for Moneyline, Spread and Totals straight bets | Book X’s actual margin. Nobody’s models are so perfect that they keep all of the overround. In this example we’re saying Book X holds 50% of the overround/its theoretical margin | Book X’s actual margin expressed as an average price |

-110.5 | 50% | -105.25 |

#### Step 2 – How straight bet margin becomes parlay margin

Book X’s margin for straight bets expressed as an average price | Book X’s margin on a four leg parlay is -121, expressed as an average price, or 17.4% using the more familiar GGR percentage (21 / 121 = 17.4%) |

-105.25 | -105.25 + -105.25 + -105.25 + -105.25 = -121 |

#### Step 3 – Applying the HoldCrunch Percentage to parlays

Book X’s margin for straight bets | Book X’s HoldCrunch Percentage | Book X’s straight bet margin if it were to equal the leader(s) on price: -105.25 minus 33% = -103.5 | Book X’s margin on the same four leg parlay after competing with the leader(s) on straight bet prices is -114 or 12.3% GGR (14 / 114 = 12.3%) |

-105.25 | 33% | -103.5 | -103.5 + -103.5 + -103.5 + -103.5 = -114 |

You can see that Book X’s parlay margin is directly affected by what happens with straight bet margins should Book X try to equal the leader(s) on price. 12.3% vs 17.4% is a significant difference. Yes, parlays are ‘higher margin products’ but how much higher is linked to straight bet pricing and underlying margin efficiency. We have a hugely important subscriber section devoted to comparing margin efficiency across Sportsbooks. This can only be done on the HoldCrunch Platform because you need both price data and a robust methodology for applying the cost of price to margins

In summary, the HoldCrunch Percentage is an accurate reduction factor for parlay margins, not just straight bet margins, and you can use it with confidence when looking at state reports containing both straight bets and parlays in order to establish true margin performance

## A real world example of Parlays as a roll up of Straight bets using DraftKings’ ‘Quick SGP’ product

Odds of +145 are being offered for the parlay ‘Eagles moneyline and Over 48.5’

Let’s create the same parlay from the two individual straight bets offered at the same time on DraftKings’ site. If I make a moneyline straight bet on the Eagles for $100 at the odds shown, -310, and they win, I make $32 profit ($100/310 = $32)

In my parlay bet, my original $100 stake and $32 winnings are then applied to the Over 48.5 at the straight bet price of -117. Let’s assume I win that second leg; I now receive an additional $113 profit ($132/117 = $113)

My original stake in the parlay bet was $100 and my total winnings were $145 because the odds were +145. My winnings from the two straight bets are identical ($32 + $113 = $145). This parlay is simply a combination of the two straight bets

Note: This is an example of an exact match between two straight bets and a parlay. Even where the parlay contains additional margin to individual bets, the underlying straight bet margins are a significant part of the overall parlay profit because the straight bet odds are an input into the parlay offering

## What about Props, Micro bets? Do your calculations take account of these bet types?

At the moment the HoldCrunch Percentage does not take account of Props and Micro bets. We plan to add these in the future. In the meantime we believe our data is an accurate representation of the price experience of customers and the margin impact of price on operators for these reasons:

- We analyse prices and lines across the largest US sports: NBA, NCAAB, MLB, NFL and NCAAF
- Our market coverage (Moneyline, Spread and Totals) for these major US sports accounts for 45% to 55% (depending on time of year) of the total handle across all bet types and sports
- There are margin relationships between Moneyline, Spread and Totals markets and many parlay types. HoldCrunch data is therefore applicable to parlays as well as straight bets (see the parlay example directly above)
- We of course use data from states that report at an operator level because we’re assessing operator performance. States that report handle and GGR at an operator level represent 75.79% of all US states with legal sports betting by population (data is from publicly available 2023 population estimates). States that also report NGR represent 25.46% of all US states with legal sports betting by population. Our handle, GGR+ and NGR+ data is therefore representative of the market share dynamics of US sports betting overall