For those of you who don’t know me, I’m a huge nerd when it comes to numbers and data. Heck, my educational path perfectly exemplifies this! I graduated college with a double major in mathematics and software engineering. My final project was a Blackjack game that tracked stats while using different card counting methods to show which method gave the best odds at winning money. Gathering data and geeking out over numbers is what I do.

After playing many games of SolForge, I yearned for a way to understand what my chances were at drawing my better cards.This is, after all, a numbers game. Finally, instead of hoping and wishing, I went to work. I dusted off my probability book from college. Looks like I made the right decision to keep the $100 book instead of selling it back for $5! After many hours of mathematical formulas and VBA coding, I came up with the SolForge Tracker ™.

By using this new tool, I was finally able to see what my chances were of drawing certain hands within the next 5 cards. What were my chances of seeing an all level 1 hand in player level 3? What were my odds of drawing at least two level 3 cards in the first hand of player level 3? I had all the answers, and life was good. This tool gave me some peace of mind…and at the same time enraged me even further. “OMGZROZ! Why is my opponent drawing a 16% chance hand and I swing and whiff on an 88% chance hand?! AAAAAAAAHHHHHHHHH!!!!!!11!!1!!” (Add alcohol and magnets for more rage.)

Now that I had the data, I could pinpoint exactly when games started to go awry. If my opponent was hitting his percentage hands and I wasn’t, he started seeing the better board positions (and ultimately wins). The same was true when I saw the better hands. I had a feeling that “bad draw luck” and “card level screw” were playing a part in a number of my games played. But I couldn’t just go around making wild claims and accusations. Time to get back to work.

This time I added some functionality to my SolForge Tracker ™. Now, every game I played would store some data in the background. That data could then be inserted into a database to track every game I played. It tracked how many cards of every level were played, who went first, who won the game, and a bunch of other stats. Once completed, I could start running queries against the data. Now I had meaningful data that I could use to make my case! No more wild accusations; I have data! It was fun watching how the game data shifted as I added more games. For the first 20 or so games that I tracked, the player who went first won about 80% of the time! That number has since changed and is nearer to 50% now that I have over 200 games in the database.

The data I was most interested in, however, was the data based around levels of cards played. Did the person who drew and played more of their level 3s win more often than the player who had bad luck and drew poorly? In the games I played, did I actually have an argument for losing to bad luck? Here’s an interesting visual.

The picture shows a linked scatterplot of the number of times the winner played a certain number of level 3 cards in comparison to the opponent. A normal distribution was added for reference. For example, the winner of 16 games played 3 more level 3 cards than the opponent. Likewise, there was only 1 game played where the winner played 3 less level 3 cards than the opponent. But what does the data as a whole mean?

Certainly there is a positive skew to the data. There are more games where the winner played more level 3s than the opponent versus playing less level 3s than the opponent. Percentage wise, 51% of games were games where the winner played more level 3s than the opponent. This is a stark contrast to the measly 14% of games where the winner played less level 3s than the opponent. If we look at just the games where there is a disparity in level 3s played (meaning both players haven’t played the same number of level 3s), the player who has played more level 3s wins 78% of the time while the player who has played less level 3s wins only 22% of the time. We can take this analysis one step further. If the disparity in level 3s played is 2 or more, the person who has played more level 3s has a staggering 90% win percentage to devastating 10% win percentage for the player who played 2 or fewer level 3s.

The other interesting thing is how the plotted data compares to a normalized distribution. If the data followed more closely to the normalized curve, you’d expect most of the data to be within 2 sigma of the mean. The would mean that for every game where the winner wins with more level 3s played, there is an opposite and equal game where the winner wins with less level 3s played. This is clearly not the case in SolForge where the winner more often than not plays more level 3s than the opponent.

Other notable stats that I’ve calculated show that on average, the winners play 4.8 level 2 cards during player level 2 versus the losers that only play 4.1 level 2 cards during player level 2. Also, for overall game data, the winners on average play 12.17 level 1 cards, 6.98 level 2 cards, and 3.12 level 3 cards compared to the losers who play an average 13.07 level 1 cards, 6.56 level 2 cards, and 2.27 level 3 cards. Now, obviously you can’t play a percentage of a card in a turn of SolForge, but the averages do make a resounding statement: on average, the winner will play more level 2 cards in player level 2 than the opponent, and that player will win the game having played more level 2s and 3s than the opponent.

In conclusion, the games I’ve played and tracked have given me some interesting stats to think about. It’s not just a matter of gut-feeling and drunken rage anymore. I have data that supports the idea that drawing and playing higher level cards will put you at a statistical advantage. But there is a caveat to this data. This data is my data; these are games I’ve played. My play style in SolForge is play for tactical 2-for-1s while taking damage early to give myself a better chance at drawing power later. Perhaps what my data is telling me is that my play style is allowing RNG to have a bigger effect on the game. Maybe my data isn’t a good indicator for what the game is as a whole!Then again, maybe not… Maybe my data is an accurate representation of how SolForge plays out as a whole. Maybe SolForge (as it stands today) is a well-disguised, glorified coin-flip where the winner of a game will most likely be the player who drew better cards. You can do your best to give yourself the best statistical chance at victory, but if the dice come up snake-eyes, you lose.

But that’s where you come in! The more people tracking data based on their games and how they play, the better! Below are links to downloading the updated version of the SolForge Tracker ™ as well as a brand spanking new database to house all your game data!

When you save the database, keep the name SolForgeTracking and remember the location of where you saved it to. Here are the steps to setting up your SolForge Tracker ™:

Open up the ProbabilityTracker Document. Click on the LAN button to update the location of the database. Click on Yes. Paste in the location of the SolForgeTracking database. (Example: C:\Users\Admin\Documents) Click OK.

Now you’re good to go! Enjoy the tool. Use it to watch how draw percentages are constantly changing. See how your play style affects the RNG in your games. When you’ve collected a lot of games of data, let me know what you’re seeing! Thanks for reading! Forge on!

(For more SolForge drafting, statistical analysis, and drunken raging, be sure to check me out at www.twitch.tv/dehboy333 and www.hitbox.tv/dehboy.)