Machine-learning engineers from the University of Bristol think they might have the master equation to predicting the popularity of a song.

The so-called Hit Potential Equation looks a little something like this:

Score = (w1 x f1) + (w2 x f2) + (w3 x f3) + (w4 x f4), etc.

Simple, right? The "w"s are "weights," or musical features like tempo, time signature, song duration, loudness and how energetic it is. By using a machine-learning algorithm, the team could mine official U.K. top-40 singles charts over the past 50 years to see how important these 23 features are to producing a hit song.

[partner id="wireduk"]Musical style doesn't stand still, and the weights have to be tweaked to match the era. In the '80s, for example, low-tempo, ballad-esque musical styles were more likely to become a hit. Plus, before the '80s, the "danceability" of a song was not particularly relevant to its hit potential.

Once the algorithm has churned out these weights it's simply a case of mining your proposed song for these exact same features (the "f"s in the equation) and working out whether they correspond to the trends of the time. This gives you a hit-prediction score.

The team at Bristol found they could determine whether a song would be a hit and, with an accuracy rate of 60 percent, predict whether a song will make it to top five or if it will never reach above position 30 in the chart.

Don't believe them? The team's website – Score A Hit – tracks its predictions and results of the upcoming U.K. charts. Top tip – Nicki Minaj's modestly named single "I'm the Best" will be a hot-scoring hit this December.

Predicting pop songs through science and algorithms has certainly been done before, with varying levels of success. Researchers at Tel Aviv University's School of Electrical Engineering mined popular peer-to-peer file sharing site Gnutella for trends, and have a success rate of about 30 percent to 50 percent in predicting the next music superstar.

The secret? Geography – by analyzing the query strings and geographic locations of Gnutella users, the team found that breakout artists had huge local followings, which lead to exponential growth as the artist took over the United States. The algorithm successfully predicted the rise of Soulja Boy.

Meanwhile, Emory University neuroscientists went straight to the source and looked at how teenage brains reacted to new music tracks. Kids ages 12 to 17 were shoved into MRI machines and asked to listen to new songs from upcoming artists on MySpace.

Three years later, and the researchers looked at the results. The kids' taste in music showed no link to a song's commercial success, but their brain scans told another story. The ventral striatum – the brain's reward region – was predictive of a song's future sales.

And then there's Hit Song Science. It uses an idea similar to Bristol University's equation, by using algorithms to analyze the world of popular music to look for trends, styles and sounds that are a popular amongst listeners. At the website Uplaya, wannabe hit-makers can upload a track and get a score. The higher the score, the better your song is.

Well, the more catchy it is, at least. The algorithm gives "I Gotta Feeling" by The Black Eyed Peas a hit score of 8.9 out 10, for example.

Bristol's study differs from previous research because of its high accuracy rate and the time-shifting perception to account for evolving musical taste. Tijl De Bie, senior lecturer in Artificial Intelligence, said, "musical tastes evolve, which means our 'hit potential equation' needs to evolve as well."

He added: "Indeed, we have found the hit potential of a song depends on the era. This may be due to the varying dominant music style, culture and environment."