A Method for “Gaming” Predictions for the D6 Generation

D6_badge_transparent_472_CroppedI have been listening to the D6 Generation for a few months now and enjoy the podcast. I highly recommend all gamers to listen to it. It’s long so great for listening to in parts during your commute or between classes or during your performance review. At the end of last year apparently they did gaming industry predictions. This year they did the same and scored the previous years results. They have a loose set of rules they use. It’s amusing. As a data scientist in my real life job I’d like to suggest an alternative system to them that will make it more of a game and more strategic.

Rule 1: Each prediction must have a quantitative final measure. This can be true or false or a verifiable number. “FFG will bring out a new line of miniatures in 2013.” That is good, it is either true or false. “FFG will bring out another full-of-fail Silverline game.” No, that doesn’t work because its not testable. The “full-of-fail” part of it is subjective. “FFG will do really well in 2013.” No, that is also subjective. “FFG sales will double in 2013” That follows the rules but unless FFG releases the numbers it is unverifiable. “Games Workshop’s stock price (GAW: LONDON) will be greater than 740GBP by the time of the next end of year show.” That one is good – It can be researched (and is adjustable in the case of stock splits.) It is resolved in time for the follow on show.

Rule 2: Each question has a value of 1 point, +1 points for each hosts that doubts it will come true, for the predictor. If the prediction fails to come true then the predictor looses that many points. Hosts may agree with the prediction and may win or lose 1 point if it comes to pass or fails to. Neutral votes are an automatic deduction of 1 point.

Example 1: Prediction “Paizo will introduce a D7 die in 2013!” Hosts, 2 and 3 say nay. Value of the prediction is 3. If it comes to pass the predictor will get 3 points. If it fails, they will lose 3 points.

Example 2: Prediction “Games Workshop will introduce a Tau Titan in 2013” Host 2 is all for it, Host 3 doubts it. Prediction is worth 2 points to the Predictor. It is worth 1 point to Host 2.

Example 3: Prediction “Dark Future will make a comeback in 2013 due to a Kickstarter project!” Host 2 is neutral. Host 3 is vehemently against such a possibility. The Predictor risks 2 points and Host 2 will automatically lose a point.

Rule 3: All information is public. If a host has inside information they must reveal source and all relevant data. This is an honor system rule.

This form of the game puts a consequence on every action. In order to score well you have to make outlandish predictions but they also have to come true. You can disagree with a prediction but that indicates it is risky and thus there should be payoff. You can agree with a prediction but you share some of its inherent risk.

It is unlikely that anyone will cast a neutral vote but the option is there to limit the gains another player/host might make. Having a penalty for risk avoidance means that the hosts have to make a priority of understanding the market and making informed decisions over just “winging it.”

I think these rules can make the little game of chance interesting. It would be really interesting to see them go back over the December 2012 predictions and rework them into the framework – rejecting soft predictions or modifying them and seeing how it plays out.

Attack of the Mutants Combat Rules

These are preliminary so may be tweeked by the time Nashcon rolls around.

AotM Combat System

Combat happens when one force enters an area containing an enemy force. Combat is fought until one side is destroyed or forced to retreat. The mutants have a different style than the “normies.” The mutants are much simpler in how they play out.

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Morituri Te Salutant analysis of Attacks

I took a hard, statistical look at the attacks in the game Morituri Te Salutant which depicts historic gladiator fighting in the 1st century. My group has been playing it at Wargames in Nashville this year and have started a campaign this past Saturday. It did not go well for my school as I lost two gladiators in the two fights I was in. So I took a look at the complex combat matrix that determines the results of comparing attacks and what I found was eye opening and useful. In one game I chose a “kick” attack even though I had many others available to me. When looking at its level of difficulty and expected damage I found it is truely one of the worst attacks in the game! This kind of understanding will help me better choose my attacks in my next game.

Difficulty is an absolute number derived from the total difficulty of the manuever and it’s likely hood of being blocked. Damage looks at all possible damage outcomes and the likelyhood the attack will be damaging. This is an absolute number. Finally the best vs. is useful to see where the attack can get you an easy hit with greater damage.

This information doesn’t preclude you from knowing an opponents weaknesses and it only looks at attacks. For example if you are up against a Sagitarius with a bow your best bed is to block all of their incoming fire with a Shield manuever if you have a shield. The Sagitarius is going to be hesitant to close with a better protected gladiator such as a Samnite.

Attack Difficulty Damage Blocked Best vs.
Lunge 61 25 33% Getup, Charge, Cut
Swing 57 32 24% Cut, Getup
Slash 59 26 29% Ssmash, Lasso
Cut 61 26 33% Swing, Slash, Getup
Thrust 52 31 24% Cut, Charge
Jab 57 18 33% Move F/Stand
Charge 56 30 24% Cut, Getup
Sh. Smash 62 17 38% Cut, Getup
Butt End 64 16 43% Jab
Lasso 55 Special 14% Getup, Reclaim
One/Two 60 26 29% Jab, Swing, Getup
Riposte 64 16 43% Lunge, Charge, Entangle
Hook 67 17 57% Lasso, Entangle
Entangle 53 Special 14% Swing, Charge, Getup
Kick 73 10 62% Getup, Reclaim
Throw 49 38 5% Getup, Cut, Reclaim, Move Back
Shoot 46 29 5% Getup, Reclaim, Move Back