First airing in 2002, The Bachelor is a titan in the world of Reality TV and has kept its most loyal viewers hooked for a full 26 seasons. To the uninitiated, the show follows 30 female contestants as they battle for the heart of a lone male bachelor, who proposes to the winner.

The contest begins the moment the women step out of a limo to meet the lead on Night One which culminates in him handing the First Impression Rose to the lady with whom he had the most initial chemistry. Over eight drama-fuelled weeks, the contestants travel to romantic destinations for their dates. At the end of each week, the lead selects one or two women for a one-on-one date, while eliminating up to five from the competition.

As self-styled mega-fans of The Bachelor, Abigail Lee and her colleagues at the University of Chicagos unofficial Department of Reality TV Engineering have picked up on several recurring characteristics in the women who tend to make it further in the competition. Overall, younger, white contestants are far more likely to succeed, with just one 30-something and one woman of color winning the leads heart in The Bachelors 20-year history a long-standing source of controversy.

The researchers are less clear on how other factors affect the contestants chances of success, such as whether they receive the First Impression Rose or are selected earlier for their first one-on-one date. Hometown and career also seem to have an unpredictable influence, though contestants with questionable job descriptions like Dog Lover, Free Spirit, and Chicken Enthusiast have rarely made it far.

For Lees team, such a diverse array of contestant parameters makes the show ripe for analysis with machine learning. In their study, Lees team compiled a dataset of contestant parameters that included all 422 contestants who participated in seasons 11 through 25. The researchers obviously encountered some adversity, as they note that they consum[ed] multiple glasses of wine per night during data collection.

Despite this setback, they used the data to train machine learning algorithms whose aim was to predict how far a given contestant will progress through the competition given her characteristics. In searching for the best algorithm, the team tried neural networks, linear regression, and random forest classification.

While the teams neural network performed the best overall in predicting the parameters of the most successful contestants, all three models were consistent with each other. This allowed them to confidently predict the characteristics of a woman with the highest probability of progressing far through the contest: 26 years of age, white, from the Northwest, works as a dancer, received her first one-on-one date on week 6, and didnt receive the First Impression Rose.

Lees team laments that The Bachelors viewership has steadily declined over the past few seasons. They blame a variety of factors, including influencer contestants (who are more concerned with growing their online following than finding true love) and the production crew increasingly meddling in the shows storylines, such as the infamous Champagne-gate of season 24.

By drawing on the insights gathered through their analysis, which the authors emphasize was done in their free time, the researchers hope that The Bachelors producers could think of new ways to shake up its format, while improving chances for contestants across a more diverse range of backgrounds, ensuring the show remains an esteemed cultural institution for years to come.

Of course, as a consolation prize, theres always Bachelor in Paradise.

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Machine learning predicts who will win "The Bachelor" - Big Think