Welcome to my PCT prediction. I applied the exact same method i used for my CT prediction for this prediction again. The thoughts behind the method are explained again in the second post for those that didnt read my CT prediction. Those who already read the CT prediction can skip straight to the numbers.

Based on a suggestion I ran 500 simulations this time to get better rounded numbers.

Having done previews in the past and enjoying reading previews of others there are two issues with most previews in my mind:

1. You don't see the quality gaps between teams. Many previews present only the final rank prediction. That is fine and i am already happy about anything but it only tells you a part of the story. Often predictors will have a couple of teams that look just about equally strong with close to no difference in between.
Whether they put a certain team at e.g. 3. or 7. can look like a rather big difference but the creator might see them as basically similar strong likely seperated by 50 or 100 points while the 2. and 3. team might be seperated by a huge gap of 200 points in the mind of the creator. Those gaps are very interesting to see imo but only few prediction format can show them. Jandal shows them a bit by grouping teams into different categories and Marco shows them with a difference in "some artificial Team-OVR" but I'm looking for a better visable gap indicator.

2. Many predictions expect teams to follow a certain team building guideline to get a good ranking predictions. Like: "You need one super good main leader, bonus points if it's a climber. You also need another at least sub-top leader in every terrain. All of them need at least one good lieutenant. etc.". I am a firm believer that quality is quality. You don't have to follow traditional thinking to make a good team. You can be succesful by simply having only many sub-top riders, you can focus on one or two terrains only and be succesful (see Gazelle, Strava, Evonik or 2014 Euskaltel). But the most important point is the quality you have in your team.

I've tried to implement these two thoughts into a formula. I feel like OVR is already a very good indicator for the quality of a rider and better than every improvised formula i could come up with so i came up with a formula that directly translates the overall of a rider into expected points. This formula comes straight from the 2016 final rankings of the division.

Adding the best 20 riders (=min number needed) of each team together leads to the pure-OVR based prediction. Every rider over that limit would reduce the RDs used of the better rider so I decided to ignore them to have a fair basis to treat the different teams. (it can be a nice help in some situation to have an additional specialized helper but in most cases it doesnt matter much whether #20 or #21 is the filler in the race)

This is the pure-OVR based prediction:

#	Team	Manager	xPoints
1	Orange Pro Cycling	CrueTrue	3596
2	Berg Cycles	fintas	3451
3	Meiji - JR East	valverde321	2954
4	Isostar - Adriatic	Croatia14	2787
5	Eritel - Sonatrach	SportingNonsense	2741
6	Newton Foundation	Mresuperstar	2740
7	Indosat Ooredoo - ANZ	Eden95	2728
8	Podium Ambition	TheManxMissile	2722
9	Team UBS	Bushwackers	2719
10	Ferrero - Samruk	Spilak23	2718
11	SPAR - Shimano - SCG	Bikex	2659
12	Grieg - Eftel	tastasol	2616
13	Fablok - Bank BGZ	sgdanny	2490
14	Minions	Marcovdw	2441
15	Novatek-Panarmenian.net	Selwink	2418
16	Kraftwerk Man Machine	DaveTwoBob	2414
17	Generali - EDF	matt17br	2408
18	MOL - OMV Petrom	jaxika	2404
19	Compal-Merida	Bjartne	2313
20	Carlsberg - Danske Bank	baseballlover312	2123
21	Euskaltel	Luis Leon Sanchez	2005
22	Haute Route - Mavic	the_hoyle	1881
23	Lierse SK - Pizza Ullo PCTeam	Ollfardh	1863
24	Team Ticos Air Costa Rica	ste117	1847
25	Valio - DeLaval	Atlantius	1760
26	Netia - Vónin	trekbmc	1445

I then went through the list of riders and adjusted expected points of many riders based on how i rate their stats and how well they performed in 2016 (measured in points). That were especially downgrades for one dimensional climbers without acceleration and 76-78 sprinters and upgrades for versatile riders like Bille, low AVG TTers like Stannard and other underrated riders like Chavanne. This step was fully suspective and since i basically looked over every rider the influence of the original formula was widely reduced but is only used as a baseline for my grading of better riders and as a grade for lesser rider. The result is a list of expected points for each rider.

I think of gained points of a rider in a season as a statistical random variable that (if we played a season often enough) would be distributed randomly around the expectated points with a certain variance. For this preview I assumed a Gaussian distribution eventhough that's obviously not really the ideal case but it is simple and good enough to work for a model like this. The variance depends on the highest main stat so that a TTist or a climber will have a higher variance than a puncheur.

I then simulated 500 seasons based on these numbers to see how likely each team is to finish in a certain spot of the rankings. The results are seen in the matrix posted below. How to read the chart:
Place = The predicted finishing position as seen in all other predictions. This should be used to compare my method with other predictions for a fair comparison.
AVG = Average finishing position of 500 simulations
1 = Observed probability to finish 1st
...
So e.g. Berg finished 17,2% of simulations as division winner and 19,2% as runner-up. Worst result in 500 simulations was a 15th place finish.

Please note: I haven't adjusted anything for the new calendar because i havent looked at the calendar too much and don't know yet how much the expected points should change. I also didnt looked at any announced race planning information to change the numbers. These numbers should simply be seen as an idea for expected finish, best case and worst case instead of any real realistic numbers.

DaveTwoBob wrote:

Would love to know how likely it is for Kraftwerk to finish in the top 10 of PCT <hint>

Ollfardh wrote:
Not sure how that works, but I seem to understand that there's a 61,6% chance we don't go down

knockout wrote:
From the CT prediction thread :



53,8 % according to my simulations

matt17br wrote:
Awesome stuff. Lovely to see this method applied to the PCT as well. Around 7th is personally in the whereabouts of where I'd place myself, so I'm glad that your formula agrees with the quick assessment I posted a couple of weeks ago

On the other hand I find it really hard to imagine Orange not coming out on top after 500 simulations, which leads me to believe the formula(s) might have overrated some of Berg's riders somehow - Costa might score even less than 2016 because of the lack of suitable races, for instance.

I'm however happy to see a team like Netia being rewarded a bit more, when they have been consistently (imho) misinterpreted by other formulas. They're dead last when calculating the median of all the current predictions - which I strongly, strongly disagree with. I think far too many predictors have been relying on OVLs as their main metric, which clearly doesn't tell the whole story at all.

Guess my explanation wasnt as good as i hoped. Personally, i'd say the core of my preview are the subjective ratings of every rider and not some formula.