reflection7-holidaymb-sean-barry

week1.md

@@ -0,0 +1,11 @@
+For the first week, I chose to write about the Dream Minecraft speedrunning drama. This is an interesting case we are looking at data that helped disprove a speedruns validity. Luck plays a part in Minecraft speedruns, specifically for item drop rates and piglin barter odds. If you have better luck with these aspects of the game, you will be able to complete more runs and be on good pace more often.
+
+The drama started after Dream, a popular Minecraft Youtuber submitted a 3rd place run to the speedrun.com leaderboards. When moderators looked into that run and others attempted by Dream on stream, they noticed something odd. It seemed that Dream had incredible luck when it came to blaze rod drop rates (50% chance to drop when killing a blaze) and ender pearl barters (~5% chance per gold traded). Here is some of the data on both scenarios.
+
+![image1](https://github.com/HolidayMB/reflections/blob/master/dream1.PNG)
+
+![image1](https://github.com/HolidayMB/reflections/blob/master/dream2.PNG)
+
+In both of these graphs, when compared to another runner (Illumina), the expected value, and the 99.9 percentile, we see that Dream's luck is far beyond what would be considered normal, or even possible. The publication of this data resulted in the removal of the run, and for the moderators to claim that Dream must have altered his game files to give him a better chance at a good run. This spiraled into a whole event with back and forths that were pretty entertaining but I won't go into that here.
+
+I think this is a cool example of data being used interestingly. Just by using data obtained from watching streams, the moderators were able to confidently disprove the validity of the run and prove that there was something else going on. It's cool that a paper was published, and it introduced a lot of people to the world of statistics and probability as a result.

week2.md

@@ -0,0 +1,8 @@
+This is an interesting representation showing when to sell your hype sneakers for the best price. Reselling sneakers is a popular way to make some money with
+relatively little effort. This data was provided by StockX, one of the most popular places to sell sneakers online, and the visualization was the winner of StockX's data contest.
+It shows based on what region, when you should sell your sneakers and to who. I never realized that where you sell them could have a measurable difference on the money made, but
+according to the data it does. This chart shows different chunks of times that indicate when to sell or hold for the best profit. I personally find this interesting because I 
+almost always sell within the first time window, which is labeled hold, meaning I must be doing something wrong. I learned a lot from this and it will definitely be helpful in 
+the future.
+
+![image1](https://github.com/HolidayMB/reflections/blob/master/shoes.png)

week7.md

@@ -0,0 +1,3 @@
+https://www.reddit.com/r/dataisbeautiful/comments/t2ssef/oc_i_visualized_the_telemetry_data_from_the_last/
+
+I found this on data is beautiful and thought that it was really cool. It's a replay of the telemetry data during an F1 race, so you can see each driver's speed, throttle, and breaking as they go around the track. I especially like how similar each driver is because they are driving as close to optimal so I would imagine that would mean they would be doing the same thing unless passing or driving defensively. It also shows some extra live info like RPM, gear, and if they have DRS on or off which is cool too. I wonder how you even get this data, seems like an interesting thing to be publicly available.